Bridging Background Knowledge Gaps in Translation with Automatic Explicitation (2023)

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.

## the best translations go beyond literal meaning a good translation is one that literally conveys the correct meaning of every word in one language in another language. . . and this is what is rewarded by translation metrics like bleu. however, the best translations go beyond the literal text @xcite , viewing translation as means to an end: enable a communicative act in the target language. this requires "going beyond" strict equivalency to consider underlying presuppositions of the respective source and target cultures @xcite . as a result, professional translators use a technique called "explicitation", an explicit realization of implicit information in the source language because it is apparent from either the context or the situation @xcite . one of the main motivations of explicitation is to convey the necessary background knowledge new suggestions -d figure 1 : examples of french-english wikiexpl. translators compensate for background knowledge gaps with explicitation in the target language. here, while underlined parts do not need to be introduced in the frenchspeaking world, english speakers may need additional information, as in the red colored text, to enhance their understanding. flags represent national identity and its associated cultural milieu rather than language. despite its utility, research on explicitation is limited: 1) current automatic metrics of translation prefer translations that precisely convey the exact literal meaning of the original source (thus penalizing the addition of "extraneous information"); 2) the lack of labeled explicitation data hampers the study of automatic generation. existing research including @xcite and @xcite is confined to the explicitation of connectives or relational coreferences in discourse translation and lacks systematic strategies to automatically detect or generate the explicitation. we take a new focus on explicitation and explore whether making necessary implicit knowledge explicit can help downstream translation. thus, we generate explicitations for culturally relevant 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 entities that should be explained in translation and to generate those explicitations. finally, we test whether our explicitations were useful through an automatic evaluation of the usefulness of explicitations with a multilingual question answering (qa) system, based on the assumption that good explicitations of culturally-for example-polish entities will increase the accuracy of a qa system. although explicitation is very rare (0.3%) in the training corpus, the collected examples are adequate 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. the term explicitation was first defined by @xcite 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". explicitation has been refined over the next decades: @xcite use 'amplification' to refer to 'additions', which are derivable from the socio-cultural context to enhance readability or to avoid misunderstanding due to ambiguity. @xcite conduct the first systematic study of explicitation focusing on structural, stylistic, or rhetorical differences between the two languages, formulating an "explicitation hypothesis" which broadly states that a translation tends to be more explicit than a corresponding non-translation. @xcite , however, find that the definition of explicitation is limited and suggests that the term should be reserved for additions in a translated text that cannot be explained by those linguistic differences. @xcite @xcite elaborate on the idea and develop blum-kulka's work, proposing four types of explicitation: obligatory, optional, pragmatic, and translation-inherent. subsequent studies @xcite @xcite further refine these definitions and categories. we focus on pragmatic explicitation in klaudy's sense, the explicitation of implicit background information 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 semantic structure (e.g., different functions of prepositions and inflections) while optional is by fluency and naturalness (cf. translationese) @xcite . the main motivation of pragmatic explicitation, on the other hand, is to produce a well-suited translation aimed at a target audience to enable a communicative act @xcite ) by bridging the general knowledge gap. we study automatic explicitation as one of the various efforts to accommodate not only linguistic but also cultural diversity, and further benefit the users of machine translation (mt) and crosscultural nlp systems on a larger scale @xcite . ## building the wikiexpl dataset of explicitations this section describes how we collect and examine naturally-occurring explicitations in bitexts commonly used as mt training data. the resulting wikiexpl corpus lets us reason about when explicitation is necessary and lets our automatic explicitation method learn how to generate it. ## automating explicitation previously, the only source of explicitation has been human translators. this section builds on the data from section 3 to explore generating explicitations automatically. ## 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 evaluations: intrinsic and extrinsic. -as each one has its own limitations but are complementary. ## intrinsic evaluation results annotators evaluate explicitations in english questions of xqb-pl on decision, generation, and integration (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 ( ## extrinsic evaluation results main results. explicitation is more effective in english qa tasks than polish on all metrics in xqbpl (figure 3a ), which indicates that our decision algorithm effectively selects entities that need explicitation and that the added information is provides useful information (complementing the intrinsic evaluation in section 6). xqb-es shows similar trends on ew and ewo while full input accuracy shows almost the same results after the explicitation in figure 3b . we attribute the different trends on full input accuracy 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 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 0.2 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 short mid long generation type 0 20 40 60 80 100 percentage 80.4% 6.5% 96.9% 17.2% -2.1% 23.0% 18.1% 0.0% 47.4% 4.8% 25.6% 25.4% generation type wise comparison dataset xqb-pl xqb-es rate increase rate ans incl rate figure 4: increase rate of ew on english qa task by different types of explicitation generation, and the rate of answer inclusion. mid turns out to be most effective generation type for both xqb-pl and xqb-es. answer inclusion rate increases as the length of additional explanation increases, while does not affect the increased rate of performance. from english qa task to polish/spanish qa task on ew. due to the structure of pyramidal difficulty within a single question and incremental input of quizbowl setting, ew metric is able to capture the benefits of automatic explicitation in more sensitive way than the full input settings. generally, answering 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. an additional comparison between explicitation and non-explicitation strengthens the validity of our decision algorithm in appendix e, figure 7 . ## conclusion and future work we introduce techniques for automatic explicitation to bridge the gap of background knowledge between the source speaker and the target audience. we present an explicitation dataset, wikiexpl, extracted from wikimatrix and annotated by human translators. we verify the effectiveness of our automatic explication with a direct evaluation by human translators and with extrinsic evaluation by using a multilingual qa and comparing its influence on different language tasks. our automatic explicitation 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, having human participants complete the qa task with and without explicitations will let us measure their usefulness more directly. another extension would be adapting a speech-to-speech simultaneous interpretation format where the lengthy explicitation will be penalized, and thus taking the context into account to have less redundant output.