ymoslem/acl-abstracts huggingface dataset:
Recent Developments in BTB-WordNet (2023)

The paper reports on recent developments in Bulgarian BTB-WordNet (BTB-WN). This resource is viewed as playing a central role with respect to the integration and interlinking of various language resources such as: e-dictionaries (morphological, terminological, bilingual, orthographic, etymological and explanatory, etc., including editions from previous periods); corpora (coming from outside or being internal - like the corpus of definitions as well as the corpus of examples to synset meanings); ontologies (such as CIDOC-CRM, DBpedia, etc.); sources of world knowledge (such as information from the Bulgarian Encyclopedia, Wikipedia, etc.). The paper also gives information about a number of applications built on BTB-WN. These are: the Bulgaria-centered knowledge graph, the All about word application as well as some education-oriented exercises.
Lexicalised and non-lexicalized multi-word expressions in WordNet: a cross-encoder approach (2023)

Focusing on recognition of multi-word expressions (MWEs), we address the problem of recording MWEs in WordNet. In fact, not all MWEs recorded in that lexical database could with no doubt be considered as lexicalised (e.g. elements of wordnet taxonomy, quantifier phrases, certain collocations). In this paper, we use a cross-encoder approach to improve our earlier method of distinguishing between lexicalised and non-lexicalised MWEs found in WordNet using custom-designed rule-based and statistical approaches. We achieve F1-measure for the class of lexicalised word combinations close to 80{\%}, easily beating two baselines (random and a majority class one). Language model also proves to be better than a feature-based logistic regression model.
Towards an RDF Representation of the Infrastructure consisting in using Wordnets as a conceptual Interlingua between multilingual Sign Language Datasets (2023)

We present ongoing work dealing with a Linked Data compliant representation of infrastructures using wordnets for connecting multilingual Sign Language data sets. We build for this on already existing RDF and OntoLex representations of Open Multilingual Wordnet (OMW) data sets and work done by the European EASIER research project on the use of the CSV files of OMW for linking glosses and basic semantic information associated with Sign Language data sets in two languages: German and Greek. In this context, we started the transformation into RDF of a Danish data set, which links Danish Sign Language data and the wordnet for Danish, DanNet. The final objective of our work is to include Sign Language data sets (and their conceptual cross-linking via wordnets) in the Linguistic Linked Open Data cloud.
Semantic Parsing and Sense Tagging the Princeton WordNet Gloss Corpus (2023)

In 2008, the Princeton team released the last version of the {\textquotedblleft}Princeton Annotated Gloss Corpus{\textquotedblright}. In this corpus. The word forms from the definitions and examples (glosses) of Princeton WordNet are manually linked to the context-appropriate sense in WordNet. However, the annotation was not complete, and the dataset was never officially released as part of WordNet 3.0, remaining as one of the standoff files available for download. Eleven years later, in 2019, one of the authors of this paper restarted the project aiming to complete the sense annotation of the approximately 200 thousand word forms not yet annotated. Here, we provide additional motivations to complete this dataset and report the progress in the work and evaluations. Intending to provide an extra level of consistency in the sense annotation and a deep semantic representation of the definitions and examples promoting WordNet from a lexical resource to a lightweight ontology, we now employ the English Resource Grammar (ERG), a broad-coverage HPSG grammar of English to parse the sentences and project the sense annotations from the surface words to the ERG predicates. We also report some initial steps on upgrading the corpus to WordNet 3.1 to facilitate mapping the data to other lexical resources.
Context-Gloss Augmentation for Improving Arabic Target Sense Verification (2023)

Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consists of 167K context-gloss pairs (about 60K positive and 107K negative pairs), collected from Arabic dictionaries. This paper presents an enrichment to the ArabGlossBERT dataset, by augmenting it using (Arabic-English-Arabic) machine back-translation. Augmentation increased the dataset size to 352K pairs (149K positive and 203K negative pairs). We measure the impact of augmentation using different data configurations to fine-tune BERT on target sense verification (TSV) task. Overall, the accuracy ranges between 78{\%} to 84{\%} for different data configurations. Although our approach performed at par with the baseline, we did observe some improvements for some POS tags in some experiments. Furthermore, our fine-tuned models are trained on a larger dataset covering larger vocabulary and contexts. We provide an in-depth analysis of the accuracy for each part-of-speech (POS).
The Open Cantonese Sense-Tagged Corpus (2023)

This paper introduces the Open Cantonese Sense-Tagged Corpus, a new and ongoing project to serve as the companion to the development of the Cantonese Wordnet. This corpus is built on top of the Cantonese Wordnet Corpus, which currently provides example sentences for most verbs in this wordnet. This paper motivates the choice of starting a sense-tagged corpus from both linguistic and educational perspectives, and discusses the current solutions to issues arisen from the sense-tagging exercise. In total, we have tagged over 5,000 concepts, with more than 3,700 direct links to the Cantonese Wordnet.
Correcting Sense Annotations Using Wordnets and Translations (2023)

Acquiring large amounts of high-quality annotated data is an open issue in word sense disambiguation. This problem has become more critical recently with the advent of supervised models based on neural networks, which require large amounts of annotated data. We propose two algorithms for making selective corrections on a sense-annotated parallel corpus, based on cross-lingual synset mappings. We show that, when applied to bilingual parallel corpora, these algorithms can rectify noisy sense annotations, and thereby produce multilingual sense-annotated data of improved quality.
A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms (2023)

This paper addresses the task of extending a given synset with additional synonyms taking into account synonymy strength as a fuzzy value. Given a mono/multilingual synset and a threshold (a fuzzy value [0{\ensuremath{-}}1]), our goal is to extract new synonyms above this threshold from existing lexicons. We present twofold contributions: an algorithm and a benchmark dataset. The dataset consists of 3K candidate synonyms for 500 synsets. Each candidate synonym is annotated with a fuzzy value by four linguists. The dataset is important for (i) understanding how much linguists (dis/)agree on synonymy, in addition to (ii) using the dataset as a baseline to evaluate our algorithm. Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate. Our evaluations show that the algorithm behaves like a linguist and its fuzzy values are close to those proposed by linguists (using RMSE and MAE). The dataset and a demo page are publicly available at https://portal.sina.birzeit.edu/synonyms.
Expanding the Conceptual Description of Verbs in WordNet with Semantic and Syntactic Information (2023)

This paper describes an ongoing effort towards expanding the semantic and conceptual description of verbs in WordNet by combining information from two other resources, FrameNet and VerbNet, as well as enriching the verbs' description with syntactic patterns extracted from the three resources. The conceptual description of verb synsets is provided by assigning a FrameNet frame which provides the relevant set of frame elements denoting the predicate`s participants and props. This information is supplemented by assigning a VerbNet class and the set of semantic roles associated with it. The information extracted from FrameNet and VerbNet and assigned to a synset is aligned (semi-automatically with subsequent manual corrections) at the following levels: (i) FrameNet frame: VerbNet class; (ii) FrameNet frame elements: VerbNet semantic roles; (iii) FrameNet semantic types and restrictions: VerbNet selectional restrictions. We then link the syntactic patterns associated with the units in FrameNet, VerbNet and WordNet, by unifying their representation and by matching the corresponding patterns at the level of syntactic groups. The alignment of the semantic components and their syntactic realisations is essential for the better exploitation of the abundance of information across resources, including shedding light on cross-resource similarities, discrepancies and inconsistencies. The syntactic patterns can facilitate the extraction of examples illustrating the use of verb synset literals in corpora and their semantic characterisation through the association of the syntactic groups with the components of semantic description (frame elements or semantic roles) and can be employed in various tasks requiring semantic and syntactic description. The resource is publicly available to the community. The components of the conceptual description are visualised showing the links to the original resources each component is drawn from.
An Experiment: Finding Parents for Parentless Synsets by Means of CILI (2023)

Identifying and correcting inconsistencies in wordnets is a natural part of their development. Focusing only on the subproblem of missing links, we aim to find automatically possible parents for parentless synsets in IS-A hierarchies of a target wordnet by means of source wordnets where target and source wordnets are in XML-format and equipped with Collaborative Interlingual Index (CILI). In this paper, we describe the algorithm and provide statistics on the possible parents of parentless synsets of the wordnets included in the study. Additionally, we investigate the suitability of the proposed potential parent synsets for correcting noun and verb synsets within the Estonian wordnet.
Extending the usage of adjectives in the Zulu AfWN (2023)

The African languages Wordnet (AfWN) for Zulu (ZWN) was built using the expand approach, which relies on the translation of concepts in the Princeton WordNet (PWN), while retaining their PWN lexical categories. In this paper the focus is on the adjective as PWN lexical category. What is considered adjectival information (provided both attributively and predicatively) in English, is usually verbalised quite differently in Zulu - often as verb or copulative constructions - as may be seen by inspecting the Zulu written forms in {\textquotedblleft}adjective{\textquotedblright} entries in ZWN. These written forms are not complete Zulu verb or copulative constructions and in order for them to be useful, tense, polarity and agreement have to be added. This paper presents a grammar-based approach to recover important morphosyntactic information implicit in the ZWN {\textquotedblleft}adjective{\textquotedblright} written forms in order to derive a tool that would assist a user of the ZWN to render and analyse correct full forms automatically as desired by the context in which an {\textquotedblleft}adjective{\textquotedblright} is used.
Linking SIL Semantic Domains to Wordnet and Expanding the Abui Wordnet through Rapid Word Collection Methodology (2023)

In this paper we describe a new methodology to expand the Abui Wordnet through data collected using the Rapid Word Collection (RWC) method {--} based on SIL`s Semantic Domains. Using a multilingual sense-intersection algorithm, we created a ranked list of concept suggestions for each domain, and then used the ranked list as a filter to link the Abui RWC data to wordnet. This used translations from both SIL`s Semantic Domain`s structure and example words, both available through SIL`s Fieldworks software and the RWC project. We release both the new mapping of the SIL Semantic Domains to wordnet and an expansion of the Abui Wordnet.
Wordnet-oriented recognition of derivational relations (2023)

Derivational relations are an important element in defining meanings, as they help to explore word-formation schemes and predict senses of derivates (derived words). In this work, we analyse different methods of representing derivational forms obtained from WordNet {--} from quantitative vectors to contextual learned embedding methods {--} and compare ways of classifying the derivational relations occurring between them. Our research focuses on the explainability of the obtained representations and results. The data source for our research is plWordNet, which is the wordnet of the Polish language and includes a rich set of derivation examples.
What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories (2023)

Language Models are the core for almost any Natural Language Processing system nowadays. One of their particularities is their contextualized representations, a game changer feature when a disambiguation between word senses is necessary. In this paper we aim to explore to what extent language models are capable of discerning among senses at inference time. We performed this analysis by prompting commonly used Languages Models such as BERT or RoBERTa to perform the task of Word Sense Disambiguation (WSD). We leverage the relation between word senses and domains, and cast WSD as a textual entailment problem, where the different hypothesis refer to the domains of the word senses. Our results show that this approach is indeed effective, close to supervised systems.
Resolving Multiple Hyperonymy (2023)

WordNet contains a fair number of synsets with multiple hyperonyms. In parent{--}child relations, a child can have only one parent (ancestor). Consequently, multiple hyperonymy represents distinct semantic relations. In order to reclassify the multiple hyperonyms, we define a small set of new semantic relations (such as function, origin and form) that cover the various instances of multiple hyperonyms. The synsets with multiple hyperonyms that lead to the same root and belong to the same semantic class were grouped automatically, resulting in semantic patterns that serve as a point of departure for the classification. The proposed changes are based on semantic analysis and may involve the redefinition of one or several multiple hyperonymy relations to new ones, the removal of one or several multiple hyperonymy relations, and rarely the addition of a new hyperonymy relation. As a result, we incorporate the newly defined semantic relations that resolve the former multiple hyperonymy relations and propose an updated WordNet structure without multiple hyperonyms. The resulting WordNet structure without multiple hyperonyms may be used for a variety of purposes that require proper inheritance.
Towards the integration of WordNet into ClinIDMap (2023)

This paper presents the integration of WordNet knowledge resource into ClinIDMap tool, which aims to map identifiers between clinical ontologies and lexical resources. ClinIDMap interlinks identifiers from UMLS, SMOMED-CT, ICD-10 and the corresponding Wikidata and Wikipedia articles for concepts from the UMLS Metathesaurus. The main goal of the tool is to provide semantic interoperability across the clinical concepts from various knowledge bases. As a side effect, the mapping enriches already annotated medical corpora in multiple languages with new labels. In this new release, we add WordNet 3.0 and 3.1 synsets using the available mappings through Wikidata. Thanks to cross-lingual links in MCR we also include the corresponding synsets in other languages and also, extend further ClinIDMap with different domain information. Finally, the final resource helps in the task of enriching of already annotated clinical corpora with additional semantic annotations.
Connecting Multilingual Wordnets: Strategies for Improving ILI Classification in OdeNet (2023)

The Open Multilingual Wordnet (OMW) is an open source project that was launched with the goal to make it easy to use wordnets in multiple languages without having to pay expensive proprietary licensing costs. As OMW evolved, the interlingual indicator (ILI)1 was used to allow semantically equivalent synsets in different languages to be linked to each other. OdeNet2 is the German language wordnet which forms part of the OMW project. This paper analyses the shortcomings of the initial ILI classification in OdeNet and the consequent methods used to improve this classification.
The User-Aware Arabic Gender Rewriter (2023)

We introduce the User-Aware Arabic Gender Rewriter, a user-centric web-based system for Arabic gender rewriting in contexts involving two users. The system takes either Arabic or English sentences as input, and provides users with the ability to specify their desired first and/or second person target genders. The system outputs gender rewritten alternatives of the Arabic sentences (provided directly or as translation outputs) to match the target users' gender preferences.
Gender-Fair Language in Translation: A Case Study (2023)

With an increasing visibility of non-binary individuals, a growing number of language-specific strategies to linguistically include all genders or neutralize any gender references can be observed. Due to this multiplicity of proposed strategies and gender-specific grammatical differences across languages, selecting the one option to translate gender-fair language is challenging for machines and humans alike. As a first step towards gender-fair translation, we conducted a survey with translators to compare four gender-fair translations from a notional gender language, English, to a grammatical gender language, German. Proposed translations were rated by means of best-worst scaling as well as regarding their readability and comprehensibility. Participants expressed a clear preference for strategies with gender-inclusive character, i.e., colon.
Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation (2023)

Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.
Gender-inclusive translation for a gender-inclusive sport: strategies and translator perceptions at the International Quadball Association (2023)

Gender-inclusive language is of key importance to the IQA, the international governing body for quadball, a mixed-gender contact sport that explicitly welcomes players of all genders. While relatively straightforward for English, the picture becomes more complicated for most of the other IQA working languages. This paper provides an overview of the strategies currently chosen by translation team leaders for different IQA languages, the factors that influenced this decision and their connection with existing research on inclusive language strategies. It further explores the awareness and attitudes of IQA translators towards those strategies and factors.
Participatory Research as a Path to Community-Informed, Gender-Fair Machine Translation (2023)

Recent years have seen a strongly increased visibility of non-binary people in public discourse. Accordingly, considerations of gender-fair language go beyond a binary conception of male/female. However, language technology, especially machine translation (MT), still suffers from binary gender bias. Proposing a solution for gender-fair MT beyond the binary from a purely technological perspective might fall short to accommodate different target user groups and in the worst case might lead to misgendering. To address this challenge, we propose a method and case study building on participatory action research to include experiential experts, i.e., queer and non-binary people, translators, and MT experts, in the MT design process. The case study focuses on German, where central findings are the importance of context dependency to avoid identity invalidation and a desire for customizable MT solutions.
Reducing Gender Bias in NMT with FUDGE (2023)

Gender bias appears in many neural machine translation (NMT) models and commercial translation software. Research has become more aware of this problem in recent years and there has been work on mitigating gender bias. However, the challenge of addressing gender bias in NMT persists. This work utilizes a controlled text generation method, Future Discriminators for Generation (FUDGE), to reduce the so-called Speaking As gender bias. This bias emerges when translating from English to a language that openly marks the gender of the speaker. We evaluate the model on MuST-SHE, a challenge set to specifically evaluate gender translation. The results demonstrate improvements in the translation accuracy of the feminine terms.
Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges (2023)

Gender inclusivity in language technologies has become a prominent research topic. In this study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal to be achieved by machine translation (MT) models, which have been found to perpetuate gender bias and discrimination. Specifically, we focus on translation from English into Italian, a language pair representative of salient gender-related linguistic transfer problems. To define GNT, we review a selection of relevant institutional guidelines for gender-inclusive language, discuss its scenarios of use, and examine the technical challenges of performing GNT in MT, concluding with a discussion of potential solutions to encourage advancements toward greater inclusivity in MT.
Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation (2023)

The vast majority of work on gender in MT focuses on {\textquoteleft}unambiguous' inputs, where gender markers in the source language are expected to be resolved in the output. Conversely, this paper explores the widespread case where the source sentence lacks explicit gender markers, but the target sentence contains them due to richer grammatical gender. We particularly focus on inputs containing person names. Investigating such sentence pairs casts a new light on research into MT gender bias and its mitigation. We find that many name-gender co-occurrences in MT data are not resolvable with {\textquoteleft}unambiguous gender' in the source language, and that gender-ambiguous examples can make up a large proportion of training examples. From this, we discuss potential steps toward gender-inclusive translation which accepts the ambiguity in both gender and translation.