Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
Authors: HyoJung Han
Jordan Lee Boyd-Graber
Marine Carpuat
Venue: EMNLP 2023 Main (2023)
Keywords: Explicitation
translation
cross‑cultural NLP
pragmatic explicitation
multi-cultural NLP
explanatory translation
TL;DR: This work introduces techniques for automatically generating explicitations, motivated by WikiExpl, a dataset that we collect from Wikipedia and annotate with human translators, and evaluated by intrinsic and extrinsic ways.

Abstract: Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain the missing context by considering cultural differences between source and target audiences. Despite its potential to help users, NLP research on explicitation is limited because of the dearth of adequate evaluation methods. This work introduces techniques for automatically generating explicitations, motivated by WikiExpl: a dataset that we collect from Wikipedia and annotate with human translators. The resulting explicitations are useful as they help answer questions more accurately in a multilingual question answering framework.

Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
HyoJung Han Computer Science University of Maryland hjhan@cs. umd. edu
Jordan Boyd-Graber
Marine Carpuat
CS,, UMIACS, iSchool, LCS University of Maryland jbgêumiacs umd. edu
Computer Science, UMIACS
University of Maryland marineêcs umd. edu
Abstract
n Source
fr.frère Dominique Villepin...
2 La veille de Noël 1800, sachant qu'...
Translations help people understand content written in another language. However, cor- rect literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain miss- ing context by considering cultural differences between source and target audiences. Despite potential help users, NLP research on ex- plicitation is limited because of the dearth of adequate evaluation methods. This work intro- duces techniques for automatically generating explicitations, motivated by WIKIEXPI.': dataset that we collect from Wikipedia and an- notate with human translators. The resulting plicitations are useful as they help answer ques- tions more accurately in a multilingual question answering framework.
3 ...''ateentt contre Charlie Hebdo...
Literal
...brother of Dominique Villepin ...
Translation
2 The day before Christmas 1800, knowing that
3 t..the attack against Charlie Hebdo...
with
...brother of the former French Prime
Explicitation
Minister Dominique de Villepin
Christmas Eve 1800, amid the French Revolution,
3 ...the attack against the French satirical newspaper Charlie Hebdo...
Figure 1: Examples of French- French-English WIKIEXPL. Translators compensate for background knowledge gaps with explicitation in the target language. Here, while un- derlined parts do not need be introduced in the French- speaking world, English speakers may need additional information, in red colored text, to enhance their understanding, Flags represent national identity and its associated cultural milicu rather than language.
1 The Best Translations Go Beyond Literal Meaning
that is generally shared among the source language speaking communities (Séguinot, 1988; Klaudy, 1993). Since the target language audience does not share the same background and cultural knowledge, translators often give an explanatory translation for better understanding, which Klaudy (1993, 1998) deñness as "pragmatic explicitation". For exam- ple, the name "Dominique de Villepin" may be well known in French community while totally un- known English speakers in which lator may detect this gap of background knowledge between two sides and translate it as "the former French Prime Minister Dominique de Villepin" instead of just "Dominique de Villepin".
A good translation one that literally conveys the correct meaning of every word in one language in another language... and this is what is rewarded by translation metries like BLEU. However, the best translations go beyond the literal text (Snell- Hornby, 1990, 2006), viewing translation as means end: enable communicative act in the tar- get language. This requires "going beyond" strict equivalency consider underlying presuppositions the respective source and target cultures (Nida and Taber, 1969, 2003).
the trans-
As a result, professional translators use tech- nique called "explicitation", an explicit realiza- tion of implicit information in the source language because it is apparent from either the context or the situation (Vinay and Darbelnet, 1958, 1995). One of the main motivations of explicitation is to convey the necessary background knowledge
Despite its utility, research on explicitation is limited: 1) current automatic metrics of transla- tion prefer translations that precisely convey the exact literal meaning of the original source (thus penalizing the addition of "extraneous informa- tion"); 2) the lack of labeled explicitation data hampers the study of automatic generation. Ex- isting research including Hoek et al. (2015) and
We release our dataset at https://githubl com/ h-

---

Lapshinova-Koltunae and Hardmeier (2017) is confined to the explicitation of connectives or re- lational coreferences discourse translation and lacks systematic strategies to automatically detect or generate the explicitation.
Klaudy (1993, 1996, 1998) elaborate on the idea and develop Blum-Kulka's work, proposing four types of explicitation: obligatory, optional, prag- matic, and translation-inherent. Subsequent stud- ies (Baker, 1996; Øverás, 1998; Dimitrova, 2005) further refine these definitions and categories.
We take new focus explicitation and ex- plore whether making necessary implicit knowl- edge explicit can help downstream translation. Thus, we generate explicitations for culturally rel- evant content, mimicing human translators. To capture when translators expliciate, we build a dataset (which we call WIKIEXPL) that collects the entities that are described differently across languages. This dataset allows us to identify en- tities that should be explained in translation and generate those explicitations. Finally, we test whether our explicitations were useful through an automatic evaluation of the usefulness of explicita- tions with a multilingual question answering (QA) system, based on the assumption that good explici- tations of culturally- for PPlishh entities will increase the accuracy of QA system.
We focus pragmatic explicitation in Klaudy's sense, the explicitation of implicit background in- formation of the source side speaker where the main purpose is to clarify information that might not be available to the target audience. Other types of explicitation focus on synthetic or stylistic changes-for example, obligatory explicitation is mainly driven by difference of syntactic and se- mantic structure (e.g., different functions of prepo- sitions and inflections) while optional is by fluency and naturalness (cf. translationese) (Klaudy, 1993, 1996). The main motivation of pragmatic explicita- tion, on the other hand, is to produce a seilesuited translation aimed at a target audience to enable a communicative act (Snell-Hombby, 2006) by bridg- ing the general knowledge gap.
Although explicitation is very rare (0.3%) in the training corpus, the collected examples are ade- quate for developing an explicitation system that helps on a task that needs explicitation: question answering. Moreover, explicitation need not be onerous or expensive: a short phrase is enough to explain obscure entities.
We study automatic explicitation as one of the various efforts accommodate not only linguis- tic but also cultural diversity, and further benefit the users of machine translation (MT) and cross- cultural NLP systems on a larger scale (Hershcovich et al., 2022; Dev et al., 2023).
3 Building the WIKIEXPL Dataset of Explicitations
2 What is Explicitation?
This section describes how we collect and examine naturally-ocouringg explicitations in bitexts com- monly used MT training data. The resulting WIKIEXPL corpus lets us reason about when ex- plicitation is necessary and lets our automatic ex- plicitation method learn how to generate it.
The term explicitation was first defined by Vinay and Darbelnet (1958) as "a procedure that consists in introducing in the target language details that remain implicit in the source language, but become clear through the relevant context or situation". Ex- plicitation has been refined over the next decades: refer 'addi-
Nida (1964) use 'amplification' tions', which are derivable from the socio-cultural context to enhance readability or to avoid misun- derstanding due to ambiguity. Blum-Kulks (1986) conduct the first systematic study of explicitation focusing on structural, stylistic, or rhetorical dif- ferences between the two languages, formulating an "explicitation hypothesis" which broadly states that translation tends to be more explicit than corresponding non-translation. Séguinot (1988), however, find that the definition of explicitation is limited and suggests that the term should be reserved for additions in a translated text that can- not be explained by those linguistic differences.
3.1 How to Find Explicitation
Finding explicitation in the parallel text is daunt- ing task, so we first need triage candidates of possible explicitation. To find explicitation exam- ples, we follow the explicitation hypothesis (Blum- Kulka, 1986) and the traits of pragmatic explicita- tion (Klaudy, 1993, 1998) mentioned in Section 2 and assume the following properties of explicita- tion in our 2
PI the Wikipedia multilingual data, we do not know whether a text and its aligned equivalents in other languages were generated by translation, from scratch in each language, or through some editing process that mixes the two. We set the direction of translation and find the explicitation under

---

1. Explicitations are part of unaligned token se- quences: an unaligned segment in the target sentence could be an explicitation, as the ex- plicitation hypothesis broadly states that a translation tends to be more explicit than a corresponding non-tran -en-trasslation (Blum-Kulka, Kulka, 1986; Pym, 2005).
to a human translators for the final explicitation annotation (Sec 3.4).
3.2 Detecting the Explicitation in Bitext
We seek to find instances of explicitations candi- dates. We first find unaligned segments u via word alignment, then we pair segment u with the clos- est entity identified by named entity recognizer (NER). Next, we determine whether the segment u is likely explicitation of entity by checking if pair of the segment and the entity e are nearly positioned within the sentence and related. Formu- lation of detection in Appendix, Algorithm
2. Explicitations are close named entities: the unaligned segment could be an explicitation if there is a named entity near the segment while its content related to the entity and helpful for bridging a background knowledge gap between source and target language users. The gap is more likely to be significant if the entity is more specific to the background or culture of the source audience.
3.3 Deciding If Explicitation is Needed
The ability to identify the entity needs explicita- tion is the key to both detecting the explicitation example and automating the explicitation process. Since our focus is on culturally sppeciff explicita- tion, we need to algorithmically define what makes an entity specific to a socio-linguisti context.
3. Explicitations are more likely for culturally distant entities: a major shift of some property values conditioned on one language to another could indicate the boundness of an entity to specific language community. For example, the Wiki page for "Dominique de Villepin" is closer to pages of French-speaking than English-speaking countries in the relative re- lational distance.
Given a relational knowledge base (KB) graph, this can be implemented as the number of hops from an entity to source and target language- speaking countries. For instance, "Dominique de Villepin" is one hop away from France in Wikipedia, but multiple hops away from English- speaking countries.
Based on these assumptions, we develop pro- cess to detect explicitation in bitext and decide whether the given entity needs explicitation or not. As a main source of explicitation examples, we choose a not-too-clean parallel corpus from Wikipedia that naturally includes unaligned seg- ments and divergence, as explicitation is by defi nition non-literal translation making implicit detail in the source explicit in the target, and thus introducing unaligned content in one of the lan- guages. If the parallel corpus is too "clean" or too parallel, it is more likely to contain literal transla- tions rather than explicitation examples.
A complementary method is compare the num- ber of incoming links to the Wikipedia page in a given language," and the length of the Wikipedia page in a given language,* as these indicate the popularity of pages in a given language.*
We implement each of these properties and mea- sure whether the property values prop of the given entity e conditioned on each language I of bitext pairs, and check if the shifts from the source lan- guage src to target language legt are above the threshold T:
prop(e [lare) prop(e) legt) >
(1)
Overall, building the WIKIEXPL dataset takes three main steps. First, we process the bitext and detect potential explicitation pairs of unaligned seg- ments and entities (Sec 3.2). Secondly, we decide if the entity in the pair needs explicitation, result- ing in the selection of candidates among the pairs (Sec 3.3). Lastly, we present extracted candidates
An entity e that passes all of these checks is con- sidered to be strongly associated with the source language community. For example, if the property shift of the closeness (negative number of hops in KB) and normalized length in Wikipedia page is
Phttps://linkcoumt
www.forggeor//annn
the assumptions we made based on the explicitation hypoth- esis. What we focus on is how some entities are discussed differently in the language of their original culture vs. another language and using that information to design explicitation strategics. More details in Limitations.
"https://(lang) http:://laanng.wiipeeddl. a. org/w/api.
*We standardize (zero mean and unit variance) the value of both properties on each language within the entities in ex- tracted WikiMatrix sentence pairs to account for the different offscts within a language.

---

meaningful enough, then the entity is considered as culturally bounded entity.
Source Language
French
Polish
Spanish
WikiMatrix
29826
21392
28900
We additionally exclude entities that are well known globally from our annotation effort even they are bound to a source community (e.g., the Eif- fel Tower in Paris), as entities that are well-known globally are less likely to require explicitation. We use the number of languages in which Wikipedia page is available for the entity measure how well-known it is. Formulation of decision in Appendix, Algorithm
Candidates
791
245
307
Top country
Annotated
460
244
220
Average
0.66
0.72
0.74
At Least one vote
236
111
73
Explicitation
116
67
44
Table I: WIKIEXPI. Construction Statistics. (prag- matic) Explicitations are rare the noisy parallel cor- pus. France, Poland, and Spain are the country that is most frequently associated with the candidate entities for each source language. Candidates are shown three annotators, and labeled as true explicitation by majority votes from the annotators. Cohen ( shows high agreement among annotators.
3.4 Annotation Framework for WIKIExPI.
To design explicitation models, we need ground truth examples and thus ask humans to manually verify the candidates automatically extracted from bitext as described above.
(1960)'s x coefficient
We present the candidates to the human annota- tors and let them label 1) whether the candidates are explicitation and 2) the span of explicitation. Annotators categorize unaligned words into "Addi- tional Information", "Paraphrase (Equivalent and no additional info)", or "Translation Error/Noise" with a focus on the "Additional Information" class that potentially contains explicitation. Annotators then determine if explicitation is present by assess- ing if the additive description explicitly explains implicit general knowledge of the source language the target language speakers. If confirmed, they mark the explicitation span both source and tar- get sentences and provide an optional note to justify their decision. Examples for each of categories and more details of the annotation framework are in Appendix B and Figure 8a.
French and Spanish, 1.052 to 1.050 for Polish. We use WikiNEuRal (Tedeschi et al., 2021) for NER and mGENRE (De Cao et al., 2022) to get the Wikidata id for named entities. We ensemble the results of alignment tools, SimAlign (Jalili Sabet et al., 2020) and awesome-align (Dou and Neu- big, 2021) to find un-aligned segments in bitext. For proximity,
decide that segment is near an entity if is within three words distance. We define the distance as a difference in the index of the tokenized words. For the relatedness between a segment and an entity, we check a segment is in the content of an entity fetched from Wikipedia.
For the decision algorithm, we set the threshold as 1 for the property of the closeness (negative num- ber of hops from entity to given language speaking country in KB), and implement it in practice by checking the existence of direct relational link to the source country. We use this one property for extracting candidates, and after the data collection. we add two more properties of the noramlized num- ber of incoming links and length of the Wikipedia page to optimize our decision function based on the collected data to have better accuracy. For entities that are well known globally, we exclude entities with Wikipedia pages in more than 250 languages." 7
Within the candidates, there are multiple nations within single linguistic milieu, particularly in the case of French and Spanish. The annotators mark candidates that come from the same country as they do, since the precision and consensus of explicitation might be sub-optimal if, for instance, we assign French entities to Canadian annotators. All annotators are translators who are fluent in both English and the source language.
Each example is annotated by three annotators and assign the label based the majority vote. We consider the candidates as final explicitation if two or more annotators agree.
'tttps: thub. com/ facebookrresaarcch/LAERR tree/main/ http//takk///i///s WWikiatrrx
'The hyperparameters are set on a development set drawn from preliminary annotation stage, which uses the same framework in the main annotation but with non-expert anno- tators (volunteer graduate and undergraduate students). The method is applied to each language and it results in the same hyperparameter values.
3.5 Experiment Settings
For the noisy parallel data, we use Wikimatrix (Schwenk et al., 2021) and extract the fr/pl/es-en pairs around the threshold of 1.051 to 1.050 for

---

Type
Source
Target
Hypernym (h)
la Sambre
the Sambre river
Occupation/Tille (o)
Javier Gurruchaga
showman Javier Gurruchaga People Party (PP)
Acronym Expansion (a)
PP
Full names (f)
Cervantes
Miguel de Cervantes
Nationality (n)
Felipe II
Philip of Spain
Integrated (1)
Dominique de Villepin
former French Prime Minister Dominique...
Table 2: Examples of explicitation are categorized into five types. Integrated are examples with two or more types of explicitation. The boldface indicates added text by explicitation. The spans of added text marked by annotators. More examples and full-sentence version available in Table and Figure 8b.
3.6 Quantitavite Analysis on WIKIEXPL
the data from Section to explore generating ex- plicitations automatically.
Explicitation quite rare (Table 1) which agrees with pragmatic explicitation statistics in Becher (2010). About 1-3% of sentences are explicita- tion candidates. France, Poland, and Spain are the country that is most frequently associated with the candidate entities for each source language. The overall ratio of the final explicitation example from the initial corpus is 0.2-0.4%
4.1 Deciding if' Explicitation is Needed
Abualadas (2015) contends translators judge the assumed target reader to decide an explicitation is needed. Simulating such decision process is
challenging as it requires both instantiating hypo- thetical reader and predicting what they know. In contrast, Section 3.3 simplifies this by providing explicitations for entities that are tightly bound the source socio-linguist context. We further opti- mize our decision function to have better accuracy by diversifying the types of properties and tuning the thresholds based on (SeeExPL.(Section 3.5)
The agreement among annotators reliable (x x 0.7) across the languages but suggests there is some subjectivity. About one-third to half of the candidates are marked as explicitation by at least one annotator.
3.7 Qualitative Analysis on WIKIEXPL
Among the collected data from all three languages. we analyze examples and categorize them into five types (Table 2): Hypernym, Occupation/Title Acronym Expansion, Full names, and Nationality. A final type, Integrated, for the examples with two or more types of explicitation. All the patterns we discovered are consistent with explicitation lit- erature (Klaudy, 1996; Baumgarten et al., 2008; Gumul et al., 2017). The most common type in our collection is adding nationality information to the entity, especially for locations.
4.2 Generating the Explanation
We explore several forms in generating the explici- tation: 1) SHORT: inserting
two words after or before the entity, 2) MID: several words or phrase integrated into the original translation in the form of appositives or parenthetical clauses 3) LONG: 1-3 sentences apart from the original trans- lation text as form of a footnote (examples in Table 3). Although SHORT and MID are in the ex- amples of explicitation in Table 2 and LONG is not, such a long explanation is also considered explicita- tion and its usual surface manifestation would be footnote (Gumul et al., 2017). We explore the valid- ity of these three generation types of explicitation and seek to find the most effective one.
Realization of explicitation diverse. The ad- ditional information could be accompanied by ad- jectives, prepositions, integrated into an appositive with commas, or in a parenthetical expression. De- tailed descriptions on each type are in Appendix C. Additional examples of each type are available in Table 8, and the full-sentence version with the com- ments from the annotators are in Figure 8b.
Our generation is grounded in Wikidata and Wikipedia- rather than free-form text generation- to prevent hallucinations and to control length the type of explanation. For SHORT explicitations, fetch a word from instance of country of from Wikidata (cfHypernym, Title, and Nationality in Table 2). For MID, we fetch a description the entity from Wikidata (mostly the Integrated
4 Automating Explicitation
Previously, the only source of explicitation has been human translators. This section builds on

---

Type
Length
Form
Example of "Sambre."
SHORT
1-2 words
Appositive
Sambre river, Sambre, river in France and Belgium, Sambre (a river in northern France and in Wallonia. Belgium. It is left-bank tributary the Meuse, which joins in the Wallonian capital Namur.)
MID
3 words-a phrase
+ Parenthetical
LONG
1-3 sentences
Footnote
Table 3: Three types generation by the length. The form of explicitation for SHORT and MID is appositive or parenthetical which directly integrates the additive description into the text, while LONG explanation is in the form of a footnote. Generation examples from our experiment in extrinsic evaluation are available in Figure 6.
Useful target QA Subject we don't know commonly
Nat Useful in target QA Subject not popular in source community
Krubińíski et al., 2021), we use multilingual QA framework to evaluate our automated explicitation. A good explicitation adds necessary information that the original sentence lacks. If that new infor- mation helps QA system get to the right answer, it useful and well-
Useful in source QA
Useful
Subject well-known
Subject well-known globally
source QA
only source community
> Need Explicitation!
Figure How we check if explicitations work: given source Polish questions, well-gemerated explicita- tions in English will improve English QA.
suggests that the information targeted. We assume that the explicitation may not be helpful if the QA system is already knowledge- able of what is added by explicitation, and thus has minimal performance changes after feeding the questions with explicitation.
Table 2).
For LONG type, we fetch three sentences from the first paragraph of Wikipedia.
Under this assumption, we hypothesize that a well-generated explicitation will increase the ac- curacy of the target language QA task while be- ing relatively less effective in the source language (Figure 2). For example, explicitation of a French cultural concept will be more useful to answer ques- tions in culturally distant language such as En- glish than in the original French. In this QA setting, "usefulness" is clearer than in the intrinsic setting: usefulness how much improves QA accuracy, which could complement subjectivity in the intrin- sic evaluation (Feng and Boyd-Graber, 2019).
5 Evaluating Explicitation
The evaluation of explicitation is challenging as how "useful" it is depends is subjective, it depends on what the hearer knows. We suggest two evalua- tions: intrinsic and extrinsic. as each one has its own limitations but are complementary.
5.1 Intrinsic Evaluation
We ask the same human annotators who identified naturally occurring explicitations (Section 3.4) if our automatic explicitation is as useful as natural ones. We evaluate Polish to English translation, with users rating the English translation. The anno- tator rates both aspects of explicitation whether the entity needed explicitation and the quality of the explicitation with three-step Likert scale: high, mid, and low. These are anchored with "not necessary" or "wrong explanation" at the low end and "appropriate and well-generate" or "necessary" at the high end. Additionally, ask the annotator if the explicitation matches the surrounding con- well: high (smoothly integrated), mid, and low (introduces grammatical error).
To see the difference in the effectiveness be- tween two QA tasks in source and target languages, we use parallel QA text. First, we identify the en- tity mention in both question texts and then see if it should be the target of explicitation with our decision function. If it is, we generate similar ex- planations for both languages and integrate them into each question text. Finally, measure the effectiveness of explicitation in both languages and see the difference between the languages.
Specifically, we use Quizbowl setup (Rodriguez et al., 2019), which deals with incremental inputs and sequential decision-making allowing amine whether the QA system is getting right word by word. Compared to entire question accu- racy in a standard QA setup, this approach allows us to analyze the effects of explicitation more pre-
ex-
5.2 Extrinsic Evaluation with Multilingual QA
Inspired by the studies using QA as extrinsic evalu- ation in cross-lingual settings (Tomita et al., 1993;

---

Num of Qs
Total Es
Explicitation
Decision
Type
Generation
Integration
XQB-pl
420
1009
62
SHORT
0.63
0.79
XQB-es
144
598
116
0.71
MID
0.82
0.92
LONG
0.95
Table 4: Statistics of XQB dataset for the evaluation. For the case of XQB-es, the 598 named entities are de- tected in both sides of Spanish and English pairs of 144 questions. Among 598 entities, our decision algorithm decides 1 116 entities need explicitation. The extraction rate of explicitation in the domain of QA dataset is higher than in the general domain.
Table 5: Intrinsic evaluation. 70% of automated explici-
tation are marked as valid decisions by human evalua- tors. The quality of generated content increases as the amount of added information gets larger. Integration of LONG is not considered as it is integrated into the form of footnote.
cisely as its use case in evaluation of simultancous interpretation (Han et al., 2022) and as in the main results (Section 7).
set 30-50 character splits for the step size, usually having 30-32 splits for one question.
Metric. We adopt the same metrics, Expected Wins (EW) and EW with oracle buzzer (EWO) as Rodriguez et al. (2019) and Han et al. (2022). Both metrics map the position of where a system answers question a number between 0 and (higher is better). The difference is when the system provides an answer: EWO assumes a QA system can answer as soon as it predicts correctly as the top-ranked hypothesis while Ew is more conservative measure that requires not just produc- ing an answer but also deciding whether to offer its guess as the answer (i.e., confidence estimation)." 9 In both cases, the number represents the probabil- ity that the system will answer before "typical" human from the Boyd-Graber et al. (2012) dataset, weighting early answers higher than later answers. Full Input Accuracy is a measurement with whole text input of a question unlike EW or EWO.
5.3 Experiment Settings
Dataset. For the parallel QA dataset, we use the Cross-lingual Quizbowl test set (Han et al., 2022, XQB). XQB-es has 148 parallel Spanish to English question pairs and XQB-pl has 512 for Polish to English. One question usually consists of 3-5 sen- tences (Table 4). We use question pairs that have named entities recognized in both source and target languages text.
Model. Our multilingual QA system is LLAMA (Touvron al., 2023) (7B for XQB-cs and 13B for XQB-pl). This model comparable or better without finetuning compared to RNN models trained on Quizbowl (Rodriguez et al., 2019) datasets (Appendix A Table 6). The input prompt is one full question, one partial question, answers, and simulated scores E [0, 1] as a guiding example, and append real question the end. An example of input prompt is in Appendix, Figure 5. We set the output of the LLAMA model to have one guess and its confidence score, and use a threshold buzzer to decide buzz, The confidence threshold for the buzzer in EW is set to 0.4 for XQB-pl and 0.8 for XQB-es, which is fit to the original text without explicitation. We accept any of the synonyms from Wikidata in either the source or target language as correct answer. We
6 Intrinsic Evaluation Results
Annotators evaluate explicitations in English ques- tions of xQB-pl I on decision, generation, and inte- gration (setup in Section 5.1, example in Figure 6). To turn the Likert scale into a single number, we interpret high as 1, mid as 0.5, and low as 0. For the quality of generation and integration, we show the result for each generation type. Annotators score the explicitation decision 0.71 (Table 5). Nega- tive examples (boldface as additional explanation added by explicitation) include too obvious ones (e.g.. "Warsaw, the capital of Poland") or those evident given the context of the sentence ("authors include the novelist Sienkiewicz").
Forr consistent segmentation between the original ques- tion text and explicitation question text, we force the split at the boundary of the entity, Additional explanation by the explicitation is treated as the same single step with the entity, regardless of their additional length For example, "Sambre river" in explicitation will be the same step as "Sambre" in the original even though there is additional text. This may not be realistic for the acoustic setting where there is time gap to deliver the added information, but we assume the text display setting where the additional text can be presented immediatcly.
The quality of generation is assessed highest on LONG type where the annotator evaluates about
"Further details of metrics are available in Rodriguez et al. (2019).

---

Expected
XOR-pi:
Accuraty
6:42
1.0
0.7
140
0.27
ciyiis brewwee Saye
20e
0.0
Incress Patelhh
6,6
Exiiiation
Herwaee late
120
6:22
9.-
0.35
0:52
73.3%
0.12
47.46
8.-
0:30
0.10
Dighna
0:35
0.32
0.5
Figft
Enyith
(a) Polish English explicitation with XQB pll dataset
XO8 es: Expected Win IEW!
MOB-es: Expected Win Oracle (EWO)
XOB es: Full Input Accuracy
0.40
1.0
0.32
nar
11.45
0.32
Increpe tamatto
120
0:25
9.6
0.52
6.1
5.1%
Pat Intutttt G
100
6:20
0,7
0:35
0.11
Driy tol
Or chal
Expliitaion
Expioration
0:35
Tale
Incrsse Bals
Spanis: bAta
Englinn OA nut
Sperion 24 tadk
Engiih CA laes
Spanis OA out
Erplas balk
(b) Spanish English explicitation with XQB-es dataset
Figure 3: Extrinsic evaluation of automatic explicitation by comparison of the effect of explicitation. The original is the performance without explicitation. A higher increase rate in the English QA task indicates the effectiveness of automatic explicitation methods. The generation type for all plots is MID.
95% the generated explanations are appropriate and useful. The quality of generation decreases with shorter explanations. The added explanation should be easy for the target reader, but sometimes the explanation itself needs an explanation: for "Sejny, city and urban gmina of Poland", the anno-
Generation Type Comparison
100
96.9%
80.4%
Dataset
XQB-pl XQB-es
Percentage
60
Rata
47.4%
Ans Incl Rate
25.8%
tators point that "town" is more familiar than "gmina" to an English audience.
18.1%
5.0%
2.1%
Short
Mid
Long
Generation Type
7 Extrinsic Evaluation Results
Figure 4: Increase rate of EW English QA task by different types of explicitation generation, and the rate of answer inclusion. MID turns out be most effective generation type for both xQB-pl and XQB-Es. Answer inclusion rate increases as the length of additional ex- planation increases, while does not affect the increased rate of performance.
Main Results. Explicitation is more effective in English QA tasks than Polish on all metrics in XQB- pl (Figure 3a), which indicates that our decision al- gorithm effectively selects entities that need explic- itation and that the added information is provides useful information (complementing the intrinsic evaluation Section 6). XQB-es shows similar trends on EW and EWO while full input accuracy shows almost the same results after the explicita- tion in Figure 3b.
from English QA task to Polish/Spanish QA task on EW. Due to the structure of pyramidal difficulty within single question and incremental input of Quizbowl setting, EW metric able to capture the benefits of automatic explicitation in more sensitive way than the full input settings. Generally, answer- ing the question early and correctly is harder than answering a question correctly given the full input, and explicitation in the middle of the question text could help QA system answer more quickly.
We attribute the different trends on full input ac- curacy for XQB-pl and xQB-es to different levels of difficulty of full-text questions: the questions that includes culturally bounded entity in XQB-es are easier than in xQB.pl. If question is already casy, then additional information would not be very helpful and thus shows small or no increase rate like in XQB-es. This is corroborated by the huge accuracy gap of 0.2 between XQB-es and XQB-pl in English QA. On the other hand, both in XQB-pl and XQB-es show great improvements of increase rate
An additional comparison between explicitation and non-explicitation strengthens the validity our decision algorithm in Appendix E, Figure 7.

---

Generation Type Comparison and Answer In- clusion Rate. We compare the generation type described in Section 4.2 and analyze the effect of answer inclusion in the explicitation (Figure 4). MID is the most effective type of explicitation, and longer explicitations are not necessarily more ef- fective but are more likely to include the answer, which differs from what we observe in intrinsic evaluation where the longer explanation tends to have a better quality of explanation. The output example of each generation type within a XQB-pl question pair is available in Figure 6, (Further anal- ysis in Appendix D, Table 7)
translation studies literature, and by evaluating ex- plicitations with both an intrinsic human evaluation and an extrinsic evaluation of their usefulness multilingual QA.
Elaboration and Clarification. The explicita- tion of implicit background knowledge resembles text simplification and question rewriting, in the sense that these techniques make the text more ac- cessible to targets, thus enhancing communications. Srikanth and Li (2021) and Wu et al. (2023) present elaborative text simplification where it adds the con- tents to elaborate the difficult concepts. Rao and Daumé III (2018, 2019) develop methods for gener- ation and reranking clarification questions that ask for information that is missing from a given context. Elgohary et al. (2019) introduces the task and the dataset of question concontext rewriting that rewrite the context-dependent question into a standalone question by making the context explicit. Ishiwatari et al. (2019) performs a task of describing unfa- miliar words or phrases by taking important clues from both "local" and "global" context, where we have in common in the methods of generating de- scription from Wikidata.
8 Related Work
Explicitation in Contemporary Works. Many of contemporary works with the term "explicitation" focus on discourse MT (Hoek et al., 2015; Webber et al., 2015) and mainly developed by Lapshinova- Koltunski et al. (2019, 2020, 2021). However, de- spite the broad coverage of the term, the explicita- tion in these studies are limited to insertion of con- nectives or annotations of coreference in the target side. Although the high-level concept of explicita- tion is common and our detection of explicitation starts similarly to Lapshinova- Kppsuinova-Koltunkii and Hard- meier (2017) by finding alignment discrepancies, our focus is on different aspects of explicitation where the main purpose to fill the gap of back- ground knowledge between source and target sides. Krüger (2020) attempts to identify instances of ex- plicitation in machine translated documents, while it deals with more general definition of explicita- tion rather than -ultuii explicitation. Explicitations can also be viewed as form of divergence in meaning between source and target text. However prior work on detecting these based on cross-lingual semantic representations (Vyas et al., 2018; Briakou and Carpuat, 2020) target much broader category of divergences than those that are eulturally relevant.
Conclusion and Future Work
We introduce techniques for automatic explicita- tion to bridge the gap of background knowledge between the source speaker and the target audience. We present an explicitation dataset, WIKIEXPL, ex- tracted from WikiMatrix and annotated by human translators. We verify the effectiveness of our auto- matic explication with a direct evaluation by human translators and with extrinsic evaluation by using multilingual QA and comparing its influence on different language tasks. Our automatic explicita- tion system is effective based on both intrinsic and extrinsic evaluation.
Future works include more closely simulating an explicitation decision and generation process of a human translator as it requires both instantiating a hypothetical reader and predicting what they might know and how to describe it while trying not to overdo it. Rather than machine QA systems, hav- ing human participants complete the QA task with and without explicitations will let us measure their usefulness more directly. Another extension would be adapting speech-to-speet
Our work also relates to contemporaneous work on culturally aware MT. Yao et al. (2023) introduce a data curation pipeline to construct a culturally spe- cific parallel corpus, and explore LLM prompting strategies to incorporate culturally specifie knowl- ege into MT. Lou and Niehues (2023) introduce semi-automatic technique to extract explanations based on Wikikpedia and alignment tools, simi- lar to ours. Our work complements these studies by grounding the definition of explicitation in the
simultancous in- terpretation format where the lengthy explicitation will be penalized, and thus taking the context into account to have less redundant output.

---

Limitations
bitext that has a clear translation process.
Our decision and generation algorithm are re- stricted to named entity based on the assmuption in Section 3.1. However, the background gap is not always related to named entities as the year "I "1800" in the second example in Figure 1. This is annotated as explicitation since French readers are well acquainted with the period of the French revolution, while non-French readers may not be. Deciding whether a non-named entity, such as a certain period of time, needs explicitation and gen- erating an explanation for it are more challenging problems than those related to named entities, and therefore, they require more advanced algorithms.
The number of explicitations samples we collected in WIKIEXPL is small, particularly when compared to the wealth of massively multilingual benchmarks that annotate relatively common language phenom- ena. Still, we argue that it provides a sufficient basis for first study of tross-lingun explicitations, a rel- atively infrequent and understudied phenomenon, valuable resource for research on translation. Furthermore, we contend that the methodology introduced can be used expand it further.
We simplify the concept of culture by choosing one majority country to which the entities of can- didate explicitation belong among extracted can- didates from Wikidata (Section 3.5 and 3.6). For example, we choose France among many French- speaking countries the number of entities from France is the largest among the extracted candi- dates. Then, we collect an explicitation example with French annotators as the accuracy and agree- ment of explicitation could be sub-optimal we ask, for example. French entity to Canadian anno- tators. As a consequence, there is possibile mis- match between languages and cultures that would degrade automated explicitation the hyperparam- eters like the threshold of properties in Section 3.2 may not be optimal for different cultures.
Our generation methods in Section 4.2 not specifically tailored for the target audience. Instead, they are designed to retrieve information using the Wikidata and Wikipedia API in a simplified man- ner. In addition to the analysis of Section 6, the annotators point out some examples have too much information integrated into one place, for exam- ple, "Augustów, Sejny, Poland", and suggest the replacement rather than the mere addition like "Au- gustów, Poland" by removing unnecessary details. This failure case clearly displays the limitation of our generation methods, where the generated ex- planation is both incorrect and needlessly adding another difficult term in the explicitation for the target audience. Another example could be ex- plaining a French-based entity, "J'accuse...!" The explanation from Wikipedia ("the open letter pub- lished by Emile Zola in response the Dreyfus affair.") barely helps target reader as it introduces another unknown information like "Emile Zola" or "Dreyfus affair". When generating explicitation, a human translator would consider the importance of the time period and decide to do recursive explicita- tion which recursively explains the not well-known words in the explicitation itself. (c.g., "Dreyfus affair, political scandal in France, 1906") These require further exploration of generation methods that need to conditioned on target readers.
The experiments are limited to one direction, into English. Our methods of automating explicita- tion and its evaluation focus on English speakers. We use simplified methods of integrating explici- tation into translation that mainly works for the English language, which could be further improved by improving fluency.
In finding the explicitation in Wikipedia, we set the direction of translation and collect the exam- ple base on the trait of explicitation that includes the unaligned tokens discussed in Section 3.1. However, there is no information about the exact translation direction, the methods of translation, or even it a non-translation and just aligned equivalents that are generated from scratch inde- pendently in each language. For example, in the given bitext of French and English, we do not know if this is translated from French to English or En- glish to French. Our focus is how the same entity is presented differently in its original cul- ture and in different language-speaking cultures, and this can be conducted without knowing the ex- act generation process of bitext. Better quality of explicitation could be collected via extracting the
Our proposed method of automating explicita- tion is grounded in structured data. It enables pre- cise control over deciding and generating explicita- tion by benefiting from consistency and enrichment of the data while avoiding hallucinations as Large Language Models (LLM). However, this approach may suffer from rigidity and limited creativity, thus having less flexibility on dealing with diverse natu- ral language input, which could lower the quality

---

of explicitation. Exploring integration of structured data and language models could be next feasible step as in Yao et al. (2023).
Eleftheria Briakou and Marine Carpuat. 2020. De- tecting
Cross- Linguul Semantic Diver- gences without Supervision by Learning to Rank. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1563-1580, Online. Association for Computa- tional I Linguistics.
Ethics Statement
The workers who annotated and evaluated the ex- plicitation candidates extracted from WikiMatrix and our automatically generated explicitation were paid at a rate of USD 0.25 per explicitation exam- ple. The resulting estimated hourly wage is above the minimum wage per hour in the United States.
Ewa S Callahan and Susan C. Herring. 2011. Cultural bias wikipedia content on famous persons, Journal of the American society for information science and technology, 62(10):18991115
J. Cohen. 1960. A Coefficient of Agreement for Nomi- nal Scales. Educational and Psychological Measure- ment, 20(1):37.
A Acknowledgment
We thank the anonymous reviewers, Pranav Goel, Sathvik Nair, Ishani Mondal, Eleftheria Briakou and the members of the CLIP lab at UMD for their insightful and constructive feedback. This work was funded in part by NSF Grants IIS-1750695, 11S IIS2244722,, and 11S- 1822444.
Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, and Fabio Petroni. 2022. Multilingual autoregressive en- tity linking. Transactions of the Association for Com- putatiooaa Linguistics, 10:274 290.
Sunipa Dev, Vinodkumar Prabhakaram, David Adelami,
Dirk Hovy, and Luciana Benotti, editors. 2023. Pro- ceedings of the First Workshop on Cross- CoNss-Cultural Considerations in NLP (C3NLP). Association for Computational Linguistics, Dubrovnik, Croatia.
References
Othman Ahmad Ali Abualadas. 2015. A linguistically- oriented approach to literary translation: A compara- tive pragmatic study of three arabic renditions of the english novel "wuthering heights".
B. Dimitrova, 2005. Expertise and Explicitation the Translation Process. Benjamins translation library: EST subseries. John Benjamins Publishing Company.
Aus Adil Abdulwahab. 2012. Necessities of pragmatic explicitation in translating english short stories into arabic. Adab AL Rafidayn, 42(61))1-33.
Zi-Yi Dou and Graham Neubig. 2021. Word alignment by fine-tuning embeddings on parallel corpora. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Lin- guistics; Main Volume, pages 2112-2128, Online. Association for Computational Linguistics.
M. Baker. 1996. Corpus-based Translation Studies: The Challenges that Lie Ahead, Benjamins Translation Library, pages 175-188. John Benjamins Publishing Company, Netherlands.
Nicole Baumgarten, Bernd Meyer, and Demet Özgetin. 2008, Explicitness in translation and interpreting: A critical review and some empirical evidence (of an elusive concept). Across Languages and Cultures, 177):37
Ahmed Elgohary, Denis Peskov, and Jordan Boyd- Graber. 2019. Can you unpack that? learning to rewrite questions-in-ccotexx. In Proceedings of the 2019 Conference on Empirical Methods in Natu- rul Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-JJCNLP), pages 2918-9244, Hong Kong. China. Association for Computational Linguistics.
Viktor Becher. 2010. Towards a more rigorous treat- ment of the explicitation hypothesis in translation studies. Trans-kom, 3(1):1-25.
Shi Feng and Jordan Boyd- Graber. 2019. What AI can do for me: Evaluating machine learning interpreta- tions in cooperative play. In International Confer- ence on Intelligent User Interfaces.
Shoshana Blum-Kulka. 1986. Shifts of cohesion and coherence in translation. The Translation Studies Reader. London/New York: Routledge, pages 209- 305.
Jordan Graber, Brianna Satinoff, He He, and Hal Daumé III. 2012. Besting the quiz master: Crowd- sourcing incremental classification games. In Pro- ceedings of the 2012 Joint Conference on Empiri- cal Methods in Natural Language Processing and Computational Natural Language Learning, pages 1300-1301, Jeju Island, Korea. Association for Com- putational Linguistics.
Ewa Gumul et al. 2017. Explicitation in simultaneous interpreting. A study into explicitating behaviour of trainee interpreters. Wydawnictwo Uniwersytetu Šlaskiego.
Scott A Hale. 2014. Multilinguals and wikipedia editing. In Proceedings of the 2014 ACM conference on Web science, pages 90-108

---

HyoJung Han, Marine Carpuat, and Jordan Boyd- Graber. 2022. SimQA: Detecting simultaneous MT errors through word-by-word question answering. In Proceedings of the 2022 Conference on Empir- ical Methods in Natural Language Processing, pages 5598-5616, Abu Dhabi, United Arab Emirates. As- sociation for Computational Linguistics.
Machine Translation, pages 495-506, Online, Asso- ciation for Computational Linguistics.
Ralph Krüger. 2020. Explicitation in neural ma- chine translation. Across Languages and Cultures, 21(2):195 221.
Ekaterina Lapshinova- Koltunski Cristina España- Bonet, and Josef van Genabith. 2019. Analysing coreference in transformer outputs. In Proceedings of the Fourth Workshop on Discourse in Machine Trans- lation (DiscoMT 2019), pages 1-12, Hong Kong, China. Association for Computational Linguistics.
Brent Hecht and Darren Gergle. 2010. The tower of babel meets web 2.0: user- geerraeeeet content and its applications in multilingual context. In Proceed- ings of the SIGCHI conference on human factors in computing systems, pages 291-300.
Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Pi- queras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, and Anders Søgaard. 2022. Challenges and strategies in cross- cultural NLP. In Proceedings of the 60th Annual Meeting of the Association for Computational Lin- guistics (Volume I: Long Papers), pages 6997-7013, Dublin, Ireland. Association for Computational Lin- guistics.
Ekaterina Lapshinova- and Christian Hard- meier. 2017. Discovery of discourse-related lan- guage contrasts through aligmment discrepancies in English-German translation. In Proceedings of the Third Workshop on Discourse in Machine Transla- rion, pages 73-81, Copenhagen, Denmark. Associa- tion for Computational Linguistics.
FEkaterina Lapshinova- Koltuukki, Marie-Pauline Krielke, and Christian Hardmeier. 2020. Coreference strategies in English- German translation. In Proceed- ings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, pages 139-153, Barcelona, Spain (online). Association for Computational I Linguistics.
Jet Hoek, Jacqueline Evers- Vermeul, and Ted J.M. Sanders. 2015. The role of expectedness in the im- plicitation and explicitation of discourse relations. In Proceedings of the Second Workshop on Discourse in Machine Translation, pages 41-46, Lisbon, Portugal. Association for Computational Linguisties.
Ekaterina Sansshnova-Kollunsst Heike Przybyl, and Yuri Bizzoni. 2021. Tracing variation in discourse connectives in translation and interpreting through neural semantic spaces. In Proceedings of the 2nd Workshop on Computational Approaches to Dis- course, pages 134-142, Punta Cana, Dominican Re- public and Online, Association for Computational Linguistics.
Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshi- naga, Graham Neubig. Shoetsu Sato, Masashi Toy- oda, and Masaru Kitsuregawa. 2019. Learning to describe unknown phrases with local and global con- texts. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume I (Long and Short Papers), pages 3467-3476, Minneapolis, Minnesota. Association for Computational Linguistics.
Renhan Lou and Jan Niehues. 2023. Audience-specific explanations for machine translation.
Paolo Massa and Federico Scrinzi. 2012. Manypedia: Comparing language points of view of wikipedia communities. In Proceedings of the Eighth Annual International Symposium on Wikis and Open Collab- oration, pages 1-9.
Masoud Jalili Sabet, Philipp Dufter, François Yvon, and Hinrich Schütze. 2020. SimAlign: High qual- ity word alignments without parallel training data using static and contextualized embeddings. In Find- ings of the Association for Computational Linguistics: EMNLP 2020, pages 1627-1643, Online. Association for Computational I Linguistics.
E.A. Nida. 1964. Toward a Science of Translating: With Special Reference to Principles and Procedures Involved in Bible Translating. E.J. Brill.
E. Nida and C.R. Taber. 1969. The theory and prac- tice of translation. Helps for translators, v. 8. United Bible Societes.
Kinga Klaudy. 1993. On explicitation hypothesis. Dániel Berzsenyi College.
Kinga Klaudy. 1996.
Back-translatte as a tool for detecting explicitation strategies in translation.
E.A. Nida and C.R. Taber. 2003. The Theory and Prac- tice of Translation. Helps for translators. Brill.
Kinga Klaudy. 1998. Explicitation. Routledge encyclo- pedia of translation studies, pages 80-84.
Anthony Pym. 2005. Explaining explicitation. New trends in translation studies. In honour of Kinga Klaudy, pages 29-34.
Mateusz Krubiáski, Erfan Ghadery, Marie-Francine Moens, and Pavel Pecina. 2021. Just ask! evalu- ating machine translation by asking and answering questions. In Proceedings of the Sixth Conference on
Sudha Rao and Hal Daumé III. 2018. Learning to ask good questions: Ranking clarification questions us- ing neural expected value of perfect information. In

---

Proceedings of the 56th Annual Meeting of the As- sociation for Computational Linguistics ( (Volume 1: Long Papers), pages 2737-2746, Melbourne, Aus- tralia. Association for Computational Linguistics.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Fric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXhv: 13977..
Sudha Rao and Hal Daumé III. 2019. Answer-based Ad- versarial Training for Generating Clarification Ques- tions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume I (Long and Short Papers), pages 145. Minneapolis, Minnesota. Association for Computational Linguistics.
Jean-Paul Vinay and Jean Darbelnet. 1958. Stylistique Comparee Du Francais Et De L' L'anglais. Biblio- theque de stylistique comparee. Paris: Didier.
Jean-Paul Vinay and Jean Darbelnct, 1995. Compara- tive Stylistics of French and English: A Methodology for Translation, volume 11 Benjamins translation library. John Benjamins Publishing Company.
Pedro Rodriguez. Shi Feng, Mohit lyyer, He He, and Jordan L. Braber. 2019. Quizbowl: The case for incremental question answering. CoRR, abs/1904.04792.
Yogarshi Vyas, Xing Niu, and Marine Carpuat. 2018. Identifying semantic divergences in parallel text with- out annotations. In Proceedings of the 2018 Confer- ence of the North American Chapter of the Associ- ation for Computational Linguisties: Human Lan- guage Technologies, Volume I (Long Papers), pages 1503-1515, New Orleans, Louisiana. Association for Computational I Linguistics.
Gabriela Saldanha. 2008.
Explicitation revisited:: Bringing the reader into the picture. Trans-kom, 1(1):20-35.
Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong, and Francisco Guzmán. 2021. Wiki- Matrix: Mining 135M parallel sentences 1620 lan- guage pairs from Wikipedia. In Proceedings of the 16th Conference of the European Chapter of the Asso- ciation for Computational Linguistics: Main Volume, pages 1351-1361, Online. Association for Computa- tional Linguisties.
Bonnie Webber, Marine Carpuat, Andrei Popescu-Bell and Christian Hardmeier, editors. 2015. Proceedings of the Second Workshop on Discourse in Machine Translation. Association for Computational Linguis- tics, Lisbon, Portugal.
Candace Séguinot. 1988. Pragmatics and the explic- itation hypothesis. TTR: traduction, terminologie, rédaction, 1(2):066-114
Yating Wu, William Sheffield, Kyle Mahowald, and Junyi Jessy Li. 2023, Elaborative simplification as implicit questions under discussion.
M. Snell-Homby. 2006. The Turns of Translation Stud- ies: New paradigms or shifting viewpoints? Ben- jamins Translation Library. John Benjamins Publish- ing Company.
Binwei Yao, Ming Jiang, Diyi Yang, and Junjie Hu. 2023. Empowering Ilm-based machine translation with cultural awareness.
Mary Snell-Homby. 1990. Linguistic transcoding or cultural transfer? critique of translation theory in germany. Translation, history and culture, 82.
Linn Øverás. 1998. In search of the third code: An investigation of norms in literary translation. Meta, 43(4):557-570.
Neha Srikanth and Junyi Jessy Li. 2021. Elaborative simplification: Content addition and explanation gen- cration in text simplification. In Findings of the Association for Computational Linguistics: ACL- IJCNLP P 2027, pages 5 5123-5137, Online. Association for Computational I Linguistics.
Algorithm 1: Detecting explicitation span
bitext
Simone Tedeschi, Valentino Maiorca, Niccolò Campol- ungo, Francesco Cecconi, and Roberto Navigli, 2021. WikiNEuRal: Combined neural and knowledge- based silver data creation for multilingual NER. In Findings of the Association for Computational Lin- guistics: EMNLP 2021, pages 2521 2531-2533, Punta Cana, Dominican Republic. Association for Compu- tational I. Linguistics.
Input:
Sentence pair (X Xegt),
= {el named entity e in X
Output: Candidate C' with unaligned
segment u and its related entity (X Xree Xigt) u), E E, E U}
U = omaligne__segmente
2 ((c, u))iis_near_and
Masaru Tomita, Masako Shirai, Junya Tsutsumi, Miki Matsumura, and Yuki. 1993. Evaluation of MT sys- tems by TOFFL. In Proceedings of the Fifth Con- ference Theoretical and Methodological Issues in Machine Translation of Natural Languages, Kyoto, Japan.
3 C = {(e, u) jeldeiid_eppiccitatoon/// #Alg2 (e, E R}
Return: C

---

Expected Wins
Expected Wins with
Full Input
Dataset
Guesser
Buzzer
Task
(FW)
Oracle Buzzer (EWO)
Accuracy
XQB-pl
Rnn
Rnn
English QA
0.35
0.70
0.62
LLaMA
Threshold
English QA
0.26
0.64
0.62
LLaMA
Threshold
Polish QA
0.17
0.56
0.43
XQB-es
Rnn
Rnn
English QA
0.30
0.65
0.48
LLaMA
Threshold
English QA
0.10
0.72
0.50
LLaMA
Threshold
Spanish QA
0.09
0.75
0.53
Table 6: Baseline performance of the original question without the explicitation. LLAMA is comparable or better without finetuning compared to RNN models trained on Quizhowl datasets.
Give your top quess with the cenfidence score to the following English questions: Question: "This countzy
crosses the Yangas region "the moat dangeroua road in the world", A moutain that overshadows the ol of Potost in this country that
winding, unpaved
dubbed
provides Large part the silver capitel city is the highest in the world and shares Lake Titimaca with its northwestern neighbour, Poru. this landlecked country in South America which has capital cities: Sucre and
Spain rich during the colorial
This country's
For 10 points, Pas."
"op gueas and its con dencee acores ("Rolivia", 0.61
"This person appears as one of the main chazacters in the detective novel, "Teoria del Manglar", written
Questioa:
Miquel Ricfsio National literssure Competition. Yoshinori Yanamoto, revealed that he had maraged to col eot more than 4,500 recordinga. Re 18 one of Ecuador's popular slager-songwritere and his Cap guess and its confidense score: ("Julio daramillo", 0.1)
Musso,
famous song is "Nuestro Jurament...
Question: "A ramoua portrait this man, created by Jaoques-noute navid, ahows pointing the sky while he prepares drink hemlock. The account of this nan's execution waa written by one of hia points, name Athenian philosopher taughs La famous for saying, I know only one thing: that know nothing"." confidence
students "Apalogy".
Cop quess
Figure 5: Example prompt input to LLAMA for multilingual QA task. This example is from XQB-es and English QA task.
Explicitation with Answer
Explicitation without Answer
Type
Metric
Orig
Expl
Orig
Expl
SHORT
EW
0.44
0.29
0.18
EWO
0.82
0.84
0.54
Algorithm 2: Deciding the necessity of ex-
FIA
0.43
0.34
MID
BW
0.29
0.26
0.04
0.15
plicitation given entity
EWO
0.66
0.71
0.48
0.50
Input: Entity e
FIA
0.65
0.85
0.43
0.39
LONG
FW
0.27
0.20
0.05
0.06
Output: True if Entity e needs explicitation else False
EWO
0.63
0.67
0.49
0.47
FIA
0.64
0.68
0.41
0,30
Param: Language Pair (Lero, ltgt), P = {prop, r) Prroperty and its threshold)
Table 7: Performance differences by explicitation in the separate case of answer inclusion in XQB-es.
for (propk, Th) do
if not (props (elopp(e)(sre)) (e)(sre) propx(ellgg) prrppeeeeeppppppeellll T()
then
A Baselines Comparison
Return False end if is (i__geera)(() then
Table 6 shows the baseline wins metric of origi- nal questions without explicitation. We also com- pare the English RNN models used in QANTA (Ro- driguez al., 2019) and SimQA (Han al., 2022). RNN has higher EW uses the guesser and the buzzer specifically trained for Quizbowl. However, EWO and Full Input Accuracy are not affected by the buzzer, so LLAMA is comparable even though
Return False Return: True

---

Ten latccch görski, bedqey jednym z najwinkszych Europie, ciagnie się przez terytorium osmiu krajöw. Ten obszar stanowi deiel wodny międay zlewiskiem Morza Baltycklego Morza Czamago oraz wyplywa niego wiele rzek, w tym Wisia. Aby otrzymac punkt, nazwij lancuch góraki, którago skiad wchodza mieday innymi Tatry, najwyzsaym szozylom jest Gerlach.
Polss Question
This mountain range, which is one of the largest in Europe, centinent, stretches across the territory of eight countries, This area is the watershed between the catchment areas of the Baltic Sea and the Black See and there are many rivers flowing out of it, including the Vistula. To get a point, name the mountain range of which the Iatras a part, and the highest peak is Gerlach.
English Queetion
Answer
Karpaty, Carpathian Mountai
Short Explictation
the Iatras, Slovakia,
Mid Expliciation
the Tatrae Imountain range on the Sloish-Stvvkk border] are
Long Expliciiation
the Tatras" are
Footnotes (For Long)
"Tarrss The Tatra Mountains, Tatres, or Tatra are series of mountais within the Westem Carpathians that form natural border between Slovakia and Poland. They are the highest mountains the Carpathians.
Figure 6: Generation example from our experiment in extrinsic evaluation in XQB-pl.
Entiless
XOO Explicitation
Nor-csplictation Entites
0.7
242
5.7
-l
Origial
143
0.7
140
122
Auli its
120
052
Incresse Tals
Ho Hoouue M
Origina
Additamal
-2
23.3%
Additoral
Orignnl
-30
b.3
0.3
0.29
8
0.3
Hocasee Roze
-00
71.2%
21.5%
0.0
Eng
Enjiihh
Polish English explicitation with XQB-pl datasct full input accuracy
X2B pl: All Entiles
XQB Explictation
XQB pl: Non explicaation Erittes
0:40
126
0.00
123
0.40
120
0.32
Crig ta
Drgins
Orggaal
0.32
Experted Baraio
0.20
0:33
2.
024
20
0.5
60
0:20
3:35
011
0:35
0.15
0:32
5:25
0.25
0.20
0:35
7.8%
0.05
7.8%
holth 06 1olk
Erglish 08 lock
PNSS 04 Disk
Bry tx
Paibh: 04 txk
Erglis bak
(b) Polish to English explicitation with XQB-pl dataset on EW
Figure 7: Comparison between the effect of additional information on all entities (left), on the entities chosen for explication (center), and the entities not chosen (right) on Polish to English XQB-pi with metric of full input accuracy and EW with generation type of MID. Higher increase rate in English task in the center plot while the opposite trends in the right and left plots indicate our decision algorithm for automatic explicitation is valid.
it is not specifically fine-tuned the task. While English and Spanish are comparable, there is an accuracy gap between English and Polish. This might suggest that the knowledge transfer between the languages may not happen effectively within a single multilingual model. Also, the gap between pl-en might be due to the difference in the amount of text seen in certain languages, Polish has smaller number of speakers compared to Spanish.
the target sentence as red while underlining the recognized named entities on both sides. We also provide the gloss in the target language with the Google Translate function for easy word-to-word comparison between source and target.
The first question we ask is to categorize the unaligned word. The annotators may select three classes of the role: 1) "Additional Information", 2) "Paraphrase (Equivalent and no additional info)" and 3) "Translation Error/Noise". Usually, the un- aligned word is "Additional Information" where the contents do not exist in the source sentence (e.g. la Sambre + the Sambre river). However, it can be mistakenly selected as an unaligned word even though there is corresponding content the source side, which falls into "Paraphrase". For ex- ample. Pan de Antas Antas breads is the case for this as both phrases are equivalent. the unalign- ment is due a translation error or if the unaligned word too divergent, we instruct the annotators
B Data Collection Frameworks in Details
After extracting the explicitation candidates from the bitext corpus, we present the candidates to the human annotators and let them label whether the candidates are explicitation or not and the span of explicitation, and this section describes the de- tails of annotation. The image of the annotation framework is in Figure 8a.
We present source and target sentences from the parallel corpus and mark unaligned segments in

---

select "Translation Noise/Error" (e.g. participa + iintroduce). We focus on the class "Additional Information" as could possibly contain real ex- plicitation which continues to the next question.
acronym is not commonly used by native speakers. Full name representation is considered as one type of cxplicitation (Klaudy, 1996). A famous per- son's 's first or last name is often omitted for conve- nience. As our annotator comments, we do not add the first name, William when mentioning Shake- speare, and likewise, there's no need to add the first name, Miguel de to Cervantes (f-1) in Spain. Full name representations (f) are marked explicita- tion as their name is famous within their country but presented in full name in English as it might not be the case outside.
The second question is to decide "Is this explic- itation". We ask annotators "Does this additive description explicitly explain the implicit general knowledge of the source language (SL.) speaker for the target language (TI.) speaking audience?". We indicate to them that this additional information is expected to be more useful to the target readers but not necessary to most speakers of the source lan- guage, and such implicit knowledge is less likely to be known by TL speaker compared to someone who is fluent in SL. familiar with SL speaking culture. This kind of general knowledge becomes explicit by the translator to enhance the reader's 's understand- ing of the translated text. For example, let's see la Sambre -> the Sambre river. This would be an example of explicitation, as it gives more context to the target audience who is not familiar with the name "Sambre" by adding the word "river" which does not exist in the source, while source language speakers may not need such explanation because it could be obvious to them. However, Jeremy + her policeman hushand Jeremy not explicitation but a simple addition of specific facts because the named entity is not famous figures or the added facts are not commonly well-known knowledge in the community of source language speakers.
The most common type is adding nationality information to the entity, especially for the location. Usually, the name of the nation is added to the name of not well-known city, providing the context information of its country.
All these types can be integrated into one ex- ample (i). Typically, the explicitation adds the nationality and its title or the hypernym for the tar- get audience who might not know who or what the entity is. The form of explicitation is diverse. The additional information could be accompanied by prepositions integrated into the form of apposi- tive with commas or in parenthetical expression.
D Analysis on Answer Inclusion Cases
Table shows the performance changes by explic- itation in the separate case of answer inclusion. The original performance before the explicitation is already higher in the case of answer including explicitation compare to without answer. This in- dicates that the answer inclusion tends to happen in easy questions, where the original question text is already evident and the additional information is highly probable include the answer because it is so obvious.
If the annotator selects yes to the second ques- tion, then we instruct them to mark the span of explicitation in both the source and target sentence and leave a note about the reason for their decision if they have any as in Figure 8b.
C Types of Explicitation-Full Examples and Analysis
E Comparison between Explicitation and Non-explicitation
Table 8 shows additional examples of explicita- tion categorized into five types from Table 2. One representative case of explicitation is intro- ducing the hypernym of the entity (h) or adding titles/occupations to the human name (o). While the name representation alone might be clear for source language speakers due to its familiarity, the target audience may lack awareness or familiarity with the name.
In Figure 7, we specifically examine the validity of the decision algorithm by comparing the influ- ence of additional information in all named entities (left) and in non-explicnn expliiiiiittioon (right) to those in the explicitation (center). Here, the non-explicitation indicates the entities with additional information that is not considered be needed to do explic- itation. In the explicitation results (center), the increase rate of the English QA task is higher than that of the Polish one, while additional information in all entities (left) and non-explicitation (right) is
Acronym Expansion (a) is also common types of explicitation (Baumgarten et al., 2008; Gumul et al., 2017). However, not all acronym expan- sion is marked as explicitation by annotators the

---

#
Type
Source
Target
(1) Hypernym (h)
la Sambre
the Sambre river
Belwederze
Belweder Palace village of Pleszów
Pleszowa
(1)
Occupation/
Javier Gurruchaga
showman Javier Gurruchaga
2
Title (o)
Jana III
king John IIII Sobieski
Juan Carlos de España
Prince Juan Carlos of Spain People 's Party (PP)
(1) Acronym
PP
2 Expansion (a)
TVE
Televisión Española of Spain (TVE)
PCE
Communist Party of Spain
(4)
PSL
Polish People Party
(1) Full names (f)
Cervantes
Miguel de Cervantes
(2)
Pitsudskiego
Józef Pitsudski
(3)
Sierakowa
Sieraków Wielkopolski
(I) Nationality (n)
Felipe II
Philip Il of SPAIN
Toruniu
Toruń (Poland)
Cuenca
Cuenca in Spain
Troyes
Troyes, France
(1)
Integrated (i)
Dominique de Villepin
former French Prime Minister Dominique de Villepin
(2)
El País
Spanish newspaper El País
(3)
Zygmunt III Waza Przekrój
Polish King Sigismund III Vasa
(4)
Polish weekly magazine Przekrój
Table 8: Additional examples of explicitation from Table 2. five types. Integrated is for the examples that include two or more types of explicitation. The boldface indicates added text by explicitation. The spans of added text are marked by annotators. Full-sentence version available in Figure 8b.
We categorize the collected explicitation example into
effective in Polish QA task in both full input accuracy and EW metrics. This demonstrates the effectiveness of our algorithm in determining the need for explicitation.
and the consumption of the contents largely varied not just by language but also by culture. An epitomic example would be the multilinguality in Wikipedia and its differences content across languages. Callahan and Herring (2011) 1) specify a cultural bias in the content, especially if it is related to famous people. Massa and Scrinzi (2012) em- phasize differences in perspectives among diverse Wikipedia communities in distinct languages. Hale (2014) and Hecht and Gergle (2010) find a "sur- prisingly" small degree of content overlap between different languages Wikipedia. Still, various efforts have been made to reflect these cultural va- rieties in the data and the models (Dev et al., 2023), As one of the various efforts to accommodate cul- tural diversity better to serve users of cross-cultural NLP systems, we explore the possibility of auto- matic explicitation to be beneficial which could help bridge the cultural gap between the language user communities.
F
Additional Related Work
Studies on pragmatic explicitation. There are several succeeding research that studies on prag- matic explicitation (Saldanha, 2008; Becher, 2010; Adil Abdulwahab, 2012). Adil Abdulwahab (2012) emphasizes the need for pragmatic explicitation in the translation of English short stories into Ara- bic. Becher (2010) did rigorous search of every type of explicitation lar scientific magazine articles and their German translations and find that pragmatic explicitation is rare, which corresponds to our empirical results in Scction 3.6.
corpus English popu-
larger scale
Cross-cultural NLP and Wikipedia. Despite the impressive gains in NI.P fields, Hershcovich al. (2022) identifies the intractable challenges in cross- cultural NLP by pointing out that the production

---

Underinne
barte Enikz
underfieed
Please clossilyy closest role of worde English when you compare the sentence between a source and target
I'Additioal Information, Explictation? (mooe details in note) [1]
explictiation specity with "Barackett" that explicitaion happens in the source and larget.
Art you thon this la eoplic tntoo why do you thine this is userul or not usaria)
Red
Unalgged Enigiial words near the Namned Enily will Hightited as Red.
it
Los travaux commencent sur une ligne ouvrages de campagne
Bource
(ace de la forteroase, avee ses points de terminalion sur la Bantkos sccks par vois terneatre.
pour couper forteresse
Google Trai
Larn mnstonn polita on the Bambrre to out the luttresa land access.
The work begins on a line of country works in front
fortisss, with is
Spen seredaation rocoppeeee: Pheaze check
works that front fortress, with its endpointa on the Sambaa river, cutting the fortress off from ovarland access.
Work was
line
Targat
conutihed nunceer of pairs becesen couits and lirpet
(a) Annotation framework collecting explicitation example.
Mitodoéć spedzila glownle w Warszawle, gdzle mieszkala rodzing (Eelwaderze) oraz Suleidwku w dworku Millusin podarowanym Pitsudsklemmu przez tolnierzy.
Source
Google
She spent her youth mainly Warsow, where she lived with family (Belvedere) and Suleyówek the "Milusin" donated
Translate
Pisudski by soldiers.
n-2))
Target
She spent her youth mainly Warsaw, living with her family the (Belweder Palace), and Sulojöwek the cottage Milusin, which Pitsudiki received as gift from his soldiers.
Annotatar's
context the target audience who familiar with the name "Betweder" adding word Palace which does exist the source, while source language speakers may not need such explanation because sould obvious to them.
comment
Obok prezenfacji bogatych zbiorów sztuki europejskiej delekowschodnii)) , częsé centraing palacu poáwięcono pamiici (Jana III. wapanlale| przeszlodel narodowa].
Source
Google
additio presentation
collections European and Far Eastern art, the central part palace was devoted the memory
Transiate
(Jan III) and the great national past.
0-22)
Target
Besides European and Oriental art, central part of the palace displayed a commemoration of (king John Sobieski), and the glorious national past.
Annotator's
Vary clear exampla. 'Jan enough for a Polish person know It's about Sobieski and was king
comment
Source
La única cadena de televisión que pudo graber en el interior de la plaza en la noche del al 4 de junio fue (TVE).
Google
Trandlate
The only lelevision network that could record inside the square at right from June 3 lo 4 was (Ive).
a-2)
Target
The only network which was able to record shots during the night of 4 June was (Ielavisión Española of Spain (IVE
Annotator's
this casa It's a clear explictation and it's usoful bacause in the source text there is an acronym that averybody in Spain would easily identity but it's not obvious for non native speakers.
comment
Source
W dnlu 28 czorwca 1914 zwrócit sil wraz z Franciszklem Wojelklem odezwa do chlopów finansowe popleranie (PSL-. Lewica).
Google
Trenslale
On June 28, 1914, he and Franciszek Wöjcik with appeal to pessents for finenciel support (PSL-
a-44)
Target
On June 28, 1914, together with Franciszek Wojolk, he addressed the peasants with financial support for the (Polish People 's Party 'Left)*
Annotalor's
PSL a commonty known acronym and is translated to Polish Peoplels Party- this acronym frequently used in the source language community and isn't famliar to the target language speaking community. Expanding wil help the target reader's 's understanding, counts explicitation.
Ellas les regalan libro, recordando el fallscimiento grandes Ilteratura europea, (Cervantes Shakespeare hispanoamericano Inca Garcllaso.
Source
Google
They give them baok, remembering the death two greats European literauree (Corvantes) and Shakespeare and Spanish -American Ince Gercilsso.
Translate
1:(1)
Target
women give the men a book. remembering the death and burial respectively two great European literary personalities, (Miquel de Cervantes) and Shakespeare, , and the Spanish notable literary personality. Inca Garcilaso.
Annotator's comment
It's just adding name a really famous person, there's no need person know by surname. The same way that they didn't add the first name (Wiliam) when mentioned Shakespeare, there's no need to add the first name (Miguel de) to Cervantes.
En siglo y después de batalla de San Quintin que acabó el 10 de agosto de 1557, Flessa de San Lorenzo, (Felipe I decidió construir ( (San Lorenzo del Escorial on honor al santo
Source
Google
the 18th century and after the battle of San Quintin that ended on August 10, 1557, feast San Lorenzo, (Fellpe II) decided to build (San
Translate
Lorenzo dal Escorial) in honor of the saint:
n+11)
Target
the 16th century and after the Battle of Salnt Quantin that anded on Saint Laurent day AUGUST 10th 1557, ( Phillp of SPAIN ).
built the (Palace of San Lorezo del Escorial), near Madrid.
Annotator's comment
This very usefu, because for me as a native speakar It's obvious what is the San Loranzo del Escorial and who Feilps was, but non native
speakers could straggle with that.
Dans cette promotion fut également admis, aux côtés de Patrick Levare, Bichard Descoingss ( ancien directeur de I' Institut df études politiques de Paris.) ) Patrick Galouzeau de Villepin| / (Trère Dominique Villepini) ), Françona Cirelli president de BlackRock France Jean-Claude Mallet. anclan secrétaire gánéral la défense nationale Françols Asselineau deuxième de promotion Président de Union Populaire Républicains.) entre autres.
Sourca
admitted, alongeide Patrick Levayo, Richard Descoings (fommer director the Institute of Polttical Studies Paris), Patrick Galouzeau Vilepin ((brather Daminique de Vilepin)), Jean François Cirlli (President From Blackrock France). Jean Cloude Mallet (fommer secretary general of national defense), François Asselineau (second of promotion and president of the People's 's Republican Union), among others.
promotion
Google
Transtate
H(1)
this promotion was also admitted, at the sides of Patrick Levaye. Richard Descoings ( (director of the Paris Institute of Political Studies ). Patrick Galouzeau de Villepin ( (brother of the former French Prime Minister Dominique de, Villepin) ). François Penyy General secretary of profecture of the Gironda), ), Jean-François Cirelli (president of Gaz de France) -lan-Claude Mallet (former General secretary of French National defense)
Target
Annotalor's
This good example of expiiitation, previding the larget audience with the contextual information about Dominique de Vilepin, though wall-nown figurs France and requiras further explanation for Franch audianca.
(b) Full sentence version of Table 2. Brackets are the span of explicitation marked by annotators including original entities and added information while red fonts are unaligned ones. Underlines in Source and Target is named entities marked by NER model.
Figure 8: Annotation Framework and full sentence example of explicitation with annotator's comments.


Reviews:
[{"review_id": "SHrN4mHtl3", "reviewer": "Reviewer_Se4s", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "R1) “Given the very low instances of explicitation, the smaller size of the data and small number of language pairs make for weaker inferences on experimental performance.”  \n→ While the dataset may seem small, particularly when compared to the wealth of massively multilingual benchmarks that annotate relatively common language phenomena, we argue that it provides a sufficient basis for a first study of cross-lingual explicitations, a relatively infrequent and understudied phenomenon. Furthermore, we contend that the methodology we introduced can be used to expand it further. \n\nR2) “While the pol-english QA shows improvement, the spanish-english doesn’t on full input accuracy”   \n→ Taken together, the results presented in Figure 3 support the claim that automatic explicitation is useful. We first explain the take-aways from the plot, before explaining the trends observed for spanish-english full inputs and the EW results.\n\n[plot explanation] In Figure 3-a, Full Input Accuracy (third column) shows a greater increase in English QA task (73% increase rate from 0.24 to 0.42) than Polish QA task (11% increase rate from 0.29 to 0.32) in XQB-pl with Explicitation. \nHigher increase rate of performance with additional explanation (Original vs Explicitation in the plot) in English QA task than in the source language QA task indicates the effectiveness of our automatic explicitation, as this gap of increase rate suggests that the added information is more helpful in English QA task and thus explicitation is adequate (Figure 2). XQB-pl (Fig 3-a) on Full Input Accuracy shows improvement from 11% in Polish QA task to 73% in English QA task which we can tell that our automatic explicitation is adequate in XQB-pl. \nIn Figure 3-b, Full Input Accuracy shows no increase by explicitation on English QA task (0% increase rate from 0.47 to 0.47) and so does on Spanish QA task (2.2% increase rate from 0.40 to 0.41) in XQB-es with Explicitation. As mentioned, there is no improvement by explicitation in English QA task (0%) and even smaller than that in Spanish (2.2%). \n\n[Answer] → We attribute the different trends on Full Input accuracy for XQB-pl and XQB-es to different levels of difficulty of questions: the questions that includes culturally bounded entity in XQB-es are easier than in XQB-pl. If a question is already easy, then additional information would not be very helpful and thus shows a small or no increase rate like in XQB-es. This is corroborated by the huge accuracy gap of 0.20 between XQB-es and XQB-pl.\n\nThe trend is different in Expected Win (EW). Both in XQB-pl and XQB-es show great improvements of increase rate from English QA task to Polish/Spanish QA task on EW. Evaluating models with the Expected Win (EW) metrics shows the benefits of automatic explicitation on a harder task than the Full Input Accuracy settings. The EW metric captures whether a player (including the QA system) answers questions both quickly and correctly.  Answering the question early and correctly is therefore harder than answering a question correctly given the full input.\n\n\nR3) “EW/EWO and full input accuracy isn’t explained in the paper,”  \n→ We will edit the paper to fully define the metrics rather than simply cite the references. We will explain that we adopt metrics from Han et al. (2022) and Rodriguez et al. (2019) (Sec 5, L461).  The Quizbowl QA system processes question text incrementally. At each step, it outputs the current guess and its decision of whether to offer its guess as the answer, in other words, to buzz (Sec 5 L465). \nWe compute the Expected Win (\\ew{}) scores, following \\citet{rodriguez2019quizbowl}.\n\\ew{} is the expected probability of winning against an average player as a function of buzzing position and empirically estimated based on the \"typical\" human gameplay. The \\ew{} probablity is only added when the buzzer buzzes for the first time and the answer is correct as in an actual game (Sec 5 L471).\nIn our setup,  however, there is only one player, so the score calculation under the competitive setting may be strict. Therefore, we also calculate \\ew{} with an \\oracle{} buzzer (\\ewo{}) where the buzzer would buzz as soon as the guesser gets the correct answer. (Sec 5, footnote9)\nFull input accuracy means the accuracy given the full text of the question.\n\n\nR4) “Both a pro and con the explicitation is tied to structured fields from wikipedia.”  \n→ Our proposed method of automating explicitation is indeed grounded in structured data. It enables precise control over deciding and generating explicitation by benefiting from consistency and enrichment of the data while avoiding hallucinations as LLMs. However, this approach may suffer from rigidity and limited creativity, thus having less flexibility on dealing with diverse natural language input, which could lower the quality of explicitation.\nWe will investigate approaches that integrate structured data and language models in future work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* Well written and easy to follow\n* Develop a process to detect pragmatic explicitation from Wikipedia parallel corpus, propose simple interesting ideas of using wikipedia entries/ distance for detecting\n* Unique problem space\n* Show positive results in intrinsic and automatic eval", "reasons_to_reject": "* Given the very low instances of explicitation, the smaller size of the data and small number of language pairs make for weaker inferences on experimental performance. While the pol-english QA shows improvement, the spanish-english doesn’t on full input accuracy - EW/EWO and full input accuracy isn’t explained in the paper, which seems like a major omission since the performance claim is based on it. \n* Both a pro and con the explicitation is tied to structured fields from wikipedia.\n\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "This work introduces techniques for automatically generating pragmatic explicitations, to capture the different assumed knowledge of different languages. They create a collection of entities described differently across languages, referred to as WIKIEXPL, collected from Wikipedia. This dataset is used to identify entities that should be explained in translation. The extraction  is tested through an automatic evaluation with a multilingual question answering (QA) system, and intrinsically by human annotators. ", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "vYpCwgoN8B", "reviewer": "Reviewer_5DDw", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback. We will improve the quality of our work by including recent works and clearer writing. We believe that the results presented support our main findings, but we welcome more specific suggestions in making our experiments more comprehensive.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper addresses a significant translation problem by automatically generating explications, and several improvement techniques have been designed. ", "reasons_to_reject": "The innovation, illustration, and writing need further improvement, and it is necessary to provide an introduction to the recent work in the background. Moreover,  they should conduct more comprehensive experiments to prove the algorithm's performance and make the experimental results more convincing.", "questions_to_authors": "", "paper_topic_and_main_contributions": "Inspired by WIKIEXPL, the work introduces several techniques for automatically generating explications, which is significant in achieving better accuracy in a multilingual question-answering framework.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "Yes", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "o1xvPcVo1g", "reviewer": "Reviewer_SwKe", "summary": "", "questions": "Could you elaborate on specific instances where literal translations fail to convey the intended meaning due to cultural or contextual differences?\nThe paper mentions previous research on explicitation in discourse translation. Can you highlight the differences between your proposed approach and these previous studies?\nPlease explain the process of creating the WIKIEXPL dataset in more detail. How did you choose the entities for annotation? \nWhat were the criteria for selecting examples that require explicitation?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Q1) “Could you elaborate on specific instances where literal translations fail to convey the intended meaning due to cultural or contextual differences?”  \n→ Beside the example in Figure 1, Table 2 and 9, we can look at this example: \"Nombreux sont ceux qui, devant une injustice, ont écrit leur « J’accuse… ! ».\" in French where the literal English translation is \"Many are those who, in the face of an injustice, have written their ‘I accuse!’.\"\nHere the meaning of ``J'accuse...!'' in French goes beyond its literal meaning, and thus the “I accuse” translation in English is not appropriate. A human translator may add an explanatory description of “J’accuse… ! ” such as as “a famous open letter by a French novelist accusing the government in response to a miscarriage of justice.'' in the English translation sentence or in a footnote.\n\nAnother example would be \"Pozostałe 4 tys. ludzi zostało przewiezione na pobliski Majdanek.\" in Polish where the literal English translation is \"The remaining 4,000 people were transported to the nearby Majdanek.\". Polish speakers would understand that the reference to Majdanek is not simply to location name but that it was specifically a concentration camp.  Thus a human translator might translate the input as “The other 4,000 people were sent to the nearby KL Lublin / Majdanek concentration camp.” \n\nQ2) “The paper mentions previous research on explicitation in discourse translation. Can you highlight the differences between your proposed approach and these previous studies?”  \n→ Prior work has focused on explicitation of discourse markers such as discourse connectives, while we focus on explicitation that arises due to background knowledge gaps between language communities.\n\nFor instance, Hoek et al. (2015) investigate whether expectedness influences the degree of implicitation and explicitation of discourse relations. Their approach is to first extract the parallel text that contains \"because\", \"although\", and \"if\" in English, Dutch and German. Then, they manually annotate the explicitness of the connective's translation in seven categories. \n\nLapshinova-Koltunski and Hardmeier (2017) introduce an automatic method to extract examples of cross-linguistically divergent discourse structures from a corpus of parallel text, and quantitatively describe alignment discrepancies between English-German discourse-related phenomena from a language contrastive perspective.\nThey define discourse-related structures as potential elements of coreference chains that can be either personal (e.g. she) or demonstrative pronouns (e.g. this). Their approach involves tools of part-of-speech tagging and dependency parsing to spot the discourse structures of interest (\"DET\",\"PRON\"). With tagger and parser information, they extract parallel patterns with an alignment tool, and count the occurrences of the alignment discrepancy patterns (e.g. DET-nsubj → PRON-dobj). There are no manual annotations involved.\n\nQ3) “Please explain the process of creating the WIKIEXPL dataset in more detail. How did you choose the entities for annotation? What were the criteria for selecting examples that require explicitation?”  \n→ The process of creating the WIKIEXPL dataset starts with extracting the candidates based on the traits of the pragmatic explicitation described in Sec 3.1.  \nFirst, we take the WikiMatrix bitext and do the detection of explicitation (Sec3.2, Algorithm 1): We run alignment tools to find unaligned models and NER tools to find entities in the bitext. Next, we pair the unaligned segments and entities if two are within three words distance and relevant by checking if the unaligned segment is included in the entity's Wiki page or fetched explanation from Wikidata. \nSecond, we decide if the entity needs explicitation (Sec3.3, Algorithm 2): given the entity and the source/target language and knowledge graph, we see if a number of hops from the entity to source language speaking country is one and more than one for target language. Additionally, we consider globally-known entities that do not need explicitation, and exclude it from the candidate if it has more than 250 languages available for the Wikipedia page. Therefore, the criteria for selecting samples that require explicitation is cultural boundedness and global popularity.  \nNow we have extracted candidates, we show them to a translator to do the annotations (Sec 3.4). We ask them label 1) whether the candidates are explicitation and 2) the span of explicitation. If two or more annotators annotate the candidate as explicitation, then we finally label it as explicitation (statistics in Table 1).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper effectively addresses the problem of translation not only as a literal process but as a means of conveying cultural nuances and background information to ensure better understanding. The creation of the WIKIEXPL dataset, annotated with human translators, is commendable. It serves as a valuable resource for training and evaluating the proposed explicitation techniques. Strengthening the evaluation and providing a clearer link to existing research will further enhance the paper's impact.\nThe paper demonstrates the practical relevance of explicitation by evaluating its impact on multilingual question answering, a real-world use case where understanding cultural context is crucial.\n", "reasons_to_reject": "While the paper identifies the gap in research related to explicitation, it could further emphasize why current NLP approaches might struggle with it and how the proposed techniques address these limitations.", "questions_to_authors": "Could you elaborate on specific instances where literal translations fail to convey the intended meaning due to cultural or contextual differences?\nThe paper mentions previous research on explicitation in discourse translation. Can you highlight the differences between your proposed approach and these previous studies?\nPlease explain the process of creating the WIKIEXPL dataset in more detail. How did you choose the entities for annotation? \nWhat were the criteria for selecting examples that require explicitation?", "paper_topic_and_main_contributions": "This paper delves into the concept of explicitation in translations, where the goal is to go beyond literal meanings to bridge cultural gaps and improve understanding between source and target languages. Professional translators use explicitations to provide context and background knowledge to the audience of the target language. Despite its potential to enhance translation quality, NLP research on explicitation has been limited due to a lack of proper evaluation methods. This paper introduces techniques for automatically generating explicitations, supported by the creation of the WIKIEXPL dataset. The resulting explicitations are assessed for their utility in enhancing multilingual question answering systems.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "Ni9FMUGCgg", "reviewer": "Reviewer_vd7U", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "R1) “there are a lot of assumptions about explicitations built into the work, which might narrow the definition of explicitations considerably.”  \n→  It is true that we focus on pragmatic explicitation (Sec2, L118) that is one of the explicitation categories by Klaudy (1993, 1996, 1998). However, we think it is worth spotting light on even though it does not cover the all explicitation cases. Our primary aim is to enhance the understanding between the two language speakers transcending mere literal translation by addressing gaps in their background knowledge. This aligns seamlessly with our emphasis on explicitation.    \nThe three assumptions are based on the well-examined hypothesis of explicitation (Sec3.1, L152) by Blum-Kulka (1986) and the characteristics that define pragmatic explicitation, which make these “assumptions”' natural and reasonable to make:\nFirst assumption that explicitations are part of unaligned token sequences  (Sec 3.1, L157) comes with the very definition of the explicitation in general, a non-literal translation making an implicit detail in the source explicit in the target (Vinay and Darbelnet, 1958), which can be applied to most kinds of explicitation including pragmatic explicitation. Finding alignment discrepancies is commonly used to detect explicitation like in Lapshinova-Koltunski and Hardmeier (2017).  \nSecond and third assumptions that explicitations are close to culturally bounded named entities (Sec 3.1, L163-178) stem from and are grounded in the definition of pragmatic explicitation, In this context, a named entity epitomizes the prevailing form that bridges the gap between shared knowledge among communities. \nHowever, these assumptions mainly deals with addition of explanation of named entity rather than with phrase of expression like idioms or replacement which is also another manifestation form of the explicitation (e.g. Halloween → Ramadan in Adil Abdulwahab, 2012).\n\nR2) “To address this, Let annotators go over the text manually and see where explicitations would be helpful without assumptions and narrow definition. Then, count how many of those are actually provided in its translation, which could help estimate the recall.”  \n→ The suggestions would be a great idea to check if the pragmatic explicitation would be naturally occurring and really helpful, and this would be an alternative way to evaluate the decision and generation algorithm. A simple approach to implement this would be leveraging existing human translation containing explicitation. We are interested in delving into this direction as part of our future endeavors.\n\nMissing Ref) Ralph Krüge’s work  \n→ The analysis of automatic explicitation by the MT system seems closely relevant to our paper, and it would be interesting to see how the explicitation that is generated by the MT system is different from the pragmatic explicitation that is proactively generated by our algorithm. We will add further comparison analysis in our future drafts.\n\nPresentation Improvements)  information about the annotators and annotation but it is a bit scattered throughout the paper.  \n→ In our upcoming drafts, we will present the dispersed information across Sec 3.4 to Sec 3.6 and Appendix B in a more coherent manner within a single paragraph in Section 3.4.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The work introduces an interesting task that can help improve translation quality, but that has probably been ignored because it does not increase scores on the typical metrics. The dataset should be useful in enabling others to do similar work. The contributions are substantial, with both a dataset, new task and two evaluations, involving manual annotation at various steps. It should be of fairly broad interest to the MT community.", "reasons_to_reject": "My main concern is that there are a lot of assumptions about explicitations built into the work, which might narrow the definition of explicitations considerably. For example, that explicitation always pertains to explicitly and nearby mentioned entities. The authors also mention a few in the limitations, such as the need to have a link to a single country. This also cannot be easily addressed in followup work, as many of these assumptions were built into the data collection. Some further assumptions also exist in the modeling, such as what type of data to fetch from where. These assumptions also make the work fairly specific to English and to certain resources, but can be changed more easily. It is clear that some assumptions have to be made, but these were not really examined at least not in the paper. \n\nTo address this, it would have been interesting for example to have an (expert?) annotator go over a text manually, see where explicitations would be helpful (without strictly adhering to the assumptions about explicitation made by the authors) and then count how many of those are actually provided in its translation. This would help estimate recall.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper introduces the apparently novel task of adding semantic explicitations to automatic translations. A dataset of such explicitations is also built and made available by the authors, which involves detecting candidate explicitations in parallel text and selecting/annotating them. Various assumptions are made in the automated selection steps before manual annotation. \n\nIn the generation task, the explicitations are generated from knowledge bases rather than from generative language models, and they are evaluated using two tasks: an intrinsic evaluation task where the annotators evaluate them in a categorial scheme, and question answering as an extrinsic evaluation task. Different types of explicitation are distinguished. The procedure is at least partially grounded in the translation studies literature and helps to address a weakness of of MT systems that rely too much on optimizing BLEU.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "Ralph Krüger wrote a few papers on explicitation in MT which I was surprised to not see. This work is more focused on what explicitation already appears in existing MT systems but it seems like a relevant perspective.\nKrüger, R. (2020). Explicitation in neural machine translation. Across Languages and Cultures, 21(2), 195-216.", "Typos_Grammar_Style_and_Presentation_Improvements": "I tried to find out information about the annotators and annotation but it is a bit scattered throughout the paper. It would be better if it was introduced in a single place. We can get some glimpses of the procedure in the appendix but it would have been nice to see the full instructions!\n\nThere are capitalization issues in the references."}}, {"review_id": "cZXT1Zeie3", "reviewer": "Reviewer_CM4F", "summary": "", "questions": "A. On line 214, the authors introduced a threshold. How is it determined? How does it change between pairs of source and target languages? \n\nB. On line 219, the authors chose to exclude entities \"well known globally\". Why? Maybe it can be shown that by including these entities the performance downgrades. And how can you define algorithmically what a \"well-known\" entity is? Even though these entities appear in 250+ Wikipedia pages this does not mean that a reader should be aware of its existence.\n\nC. On line 148, the authors say \"We choose a not-too-clean parallel corpus from Wikipedia\". Why? How do you choose such corpora? \n\nD. On line 258, the authors say: \"For proximity, we decide that a segment is near and entity if it is within three words distance\". Why? Did they try different distances and witness a meaningful drop in performance? This study should be integrated. \n\nE. On line 263, the authors say: \"For the decision algorithm, we set the threshold as 1 for the property of the number of hops, which \"usually\" selects the entities that have direct relational links to the source country\". Like for question 4, why? Maybe with more hops, you can find an entity that is known to multiple countries.", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "R-1) “The dataset, as described in Section 3, seems very small.”  \n→  While the dataset may seem small, particularly when compared to the wealth of massively multilingual benchmarks that annotate relatively common language phenomena, we argue that it provides a sufficient basis for a first study of cross-lingual explicitations, a relatively infrequent and understudied phenomenon. Furthermore, we contend that the methodology we introduced can be used to expand it further. \n\nR-2) “The motivations behind specific choices concerning the dataset and some hyperparameters are not well documented and explained,”  \n→ We will improve the description of the candidate extraction and of the dataset construction as suggested. Thank you!  \n\n2-A) “On line 214, the authors introduced a threshold. How is it determined? How does it change between pairs of source and target languages?”  \n→  The thresholds are set on a development set drawn from a preliminary annotation stage, which uses the framework described in the paper but with non-expert annotators (volunteer graduate and undergraduate students). The method is applied to each language and it results in the same hyperparameter values.\n\n2-B) ”On line 219, the authors chose to exclude entities \"well known globally\". Why? Maybe it can be shown that by including these entities the performance downgrades. And how can you define algorithmically what a \"well-known\" entity is? Even though these entities appear in 250+ Wikipedia pages this does not mean that a reader should be aware of its existence.”   \n→ We anticipate that entities that are well-known globally are less likely to require explicitation, thus we exclude them from our annotation effort. \nWe use the number of languages in which a Wikipedia page is available for this entity to quantify how well-known it is. We recognize that this is a simplification, but consider that this is a reasonable assumption in the context of Wikipedia.\n\n2-C) ”On line 148, the authors say \"We choose a not-too-clean parallel corpus from Wikipedia\". Why? How do you choose such corpora?”  \n→  If the parallel corpus is too “clean” or too parallel, it is more likely to contain literal translations rather than explicitation examples. Explicitation is by definition a non-literal translation, making an implicit detail in the source explicit in the target, and thus introducing unaligned content in one of the languages. \n\n2-D) “On line 258, the authors say: \"For proximity, we decide that a segment is near and entity if it is within three words distance\". Why? Did they try different distances and witness a meaningful drop in performance? This study should be integrated.”  \n→ We determine the distance of three based on the qualitative analysis of the sample from the preliminary annotation stage mentioned above 2-A) and the efficiency of the candidate extraction process. \nWe define a distance as a difference in the index of the tokenized words. The distance between an entity and the closest token of its explicitation segment is mostly one to two in our main target language, English. In the example of “Sambre” in Table 3, the distance can be one in the type Short, two in Mid (comma is separated by tokenizer), and three in Long (a bracket and an article are in between). The optimal distance may change according to the target language.\nOn the practical side, the distance of three is the most efficient in terms of extracting the candidates, as we use APIs to determine the necessity of explicitation (decision). Increment of the distance threshold in proximity from three to four would have slightly better recall for the candidates, however, not practical enough to cover the increased computational cost. \n\n2-E) “ On line 263, the authors say: \"For the decision algorithm, we set the threshold as 1 for the property of the number of hops, which \"usually\" selects the entities that have direct relational links to the source country\". Like for question 4, why? Maybe with more hops, you can find an entity that is known to multiple countries.”  \n→ The main motivation for using the number of hops between the entity and certain language-speaking countries in the relational graph is to see the cultural boundedness of an entity to the source language-speaking country and unboundedness to the target country.\nIn our trial with n >= 1 hops and |\\tau| >= 1 on the sample from the preliminary annotation stage mentioned above 2-A), we find that n > 1 hops lack the boundness while having quadratic increase of computational cost, and |\\tau| = 1 is enough for the boundness gap when we set the maximum number of hops to one. The parameter of |\\tau| > 1 will reinforce the shift from source to target and thus give more confidence in the decision of explicitation, however, also increase the computational burden. Therefore, we decided to set this property with n=1 hop and \\tau = -1 where it becomes checking whether the entity has a direct link to the source side and does not have it to the target side. \n\nTypo) “Table 1, the \"Explicitation-French\" cell has \"36^5\"written, but there is no footnote 5”  \n→ The actual footnote text was “only 150 are annotated.”  in Table 1 for the “French” column and “Explicitation” row, where the footnote command in a table does not appear in the paper. We will correct this error in our future draft.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. They produce a carefully generated and human-annotated dataset including qualitative and quantitative analyses.\n\n2. They perform extensive intrinsic (with human annotators) and extrinsic evaluation of the application of explicitation on the QuiBowl dataset (both Spanish-English and Polish-English), showing also an increase in performance following the metrics suited for the aforementioned dataset. \n\n3. Human translators include a variety of explicitation when translating, but current machine translation models do not. This study sheds light on this previously neglected, but useful type of information, utilizing a scientific approach to the problem.", "reasons_to_reject": "1. The dataset, as described in Section 3, seems very small. The motivation behind this statement is that the authors write: \"About 1-3% of sentences are explicitation candidates\" (row 284, subsection 3.6) and \"The overall ratio of the final explicitation example from the initial corpus is 0.1 - 0.3%\" (row 290, subsection 3.6). This seems also to be confirmed from Table 1, where the number of explicitations is 67, 44, and 36 for Polish, Spanish, and French respectively. \n\n2. The motivations behind specific choices concerning the dataset and some hyperparameters are not well documented and explained, for instance:\n    - 2.1 On line 214: the threshold introduced. \n    - 2.2 On line 219: the choice behind the fact that they exclude entities \"well known globally\".\n    - 2.3 On line 148: \"We choose a not-too-clean parallel corpus from Wikipedia\"\n    - 2.4 On line 258: \"For proximity, we decide that a segment is near and entity if it is within three words distance\".\n    - 2.5 On line 263: \"For the decision algorithm, we set the threshold as 1 for the property of the number of hops, which usually selects the entities that have direct relational links to the source country\".", "questions_to_authors": "A. On line 214, the authors introduced a threshold. How is it determined? How does it change between pairs of source and target languages? \n\nB. On line 219, the authors chose to exclude entities \"well known globally\". Why? Maybe it can be shown that by including these entities the performance downgrades. And how can you define algorithmically what a \"well-known\" entity is? Even though these entities appear in 250+ Wikipedia pages this does not mean that a reader should be aware of its existence.\n\nC. On line 148, the authors say \"We choose a not-too-clean parallel corpus from Wikipedia\". Why? How do you choose such corpora? \n\nD. On line 258, the authors say: \"For proximity, we decide that a segment is near and entity if it is within three words distance\". Why? Did they try different distances and witness a meaningful drop in performance? This study should be integrated. \n\nE. On line 263, the authors say: \"For the decision algorithm, we set the threshold as 1 for the property of the number of hops, which \"usually\" selects the entities that have direct relational links to the source country\". Like for question 4, why? Maybe with more hops, you can find an entity that is known to multiple countries.", "paper_topic_and_main_contributions": "This paper is about incorporating explicitations in translation to bridge the gaps due to the socio-cultural background between people speaking the source and target language. For instance, a well-known French politician is less likely to be known to an English-speaking audience, thus requiring additional information in the French-to-English translated text. This paper contributes twofold: they release a new dataset, WikiExpl, which is a dataset of entities described differently across languages, and then focus on explicitation by exploring whether making implicit knowledge more explicit helps downstream tasks such as multilingual question answering.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Table 1, the \"Explicitation-French\" cell has \"36^5\"written, but there is no footnote 5, I guess this is a typo."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The authors focus on explicitation in translation, which explains the missing context by considering cultural differences between source and target audiences.
They first create a dataset of naturally occurring explitations, WIKIEXPL, that they collect from Wikipedia and annotate with human translators.
Inspired by WIKIEXPL, they propose a technique to generate explicitation automatically.
They show the effectiveness of the proposed automatic explicitation method, both intrinsic evaluation by human evaluators and extrinsic evaluation by multilingual question answering tasks.

Many reviewers agree that WIKIEXPL is a valuable resource for research on translation and that there is much to learn from how they create it.
One reviewer is not impressed with the proposed automatic explicitation method because it simply extracts some structured fields from Wikipedia.

Although none of the reviewers pointed this out, the meta-reviewer thinks the proposed method has something in common with the following work, which generates a description for unknown phrases to bridge the knowledge cap.

[Ishiwatari+, NAACL-2019] Learning to Describe Unknown Phrases in Local and Global Contexts
Decision: Accept-Main