Papers by Grzegorz Kondrak
Lexical Resource Mapping via Translations (2022.lrec-1)
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| Challenge: | a lexical resource associates words with concepts in multiple languages, which makes it difficult to combine information from multiple resources. |
| Approach: | They propose a translation-based approach to mapping lexical resources . they use word-concept pairs to align WordNet/BabelNet to CLICS and OmegaWiki . |
| Outcome: | The proposed method achieves state-of-the-art accuracy without other sources of knowledge . it can be framed as word sense disambiguation, and it can improve on existing methods . |
Word Surprisal Correlates with Sentential Contradiction in LLMs (2026.eacl-long)
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| Challenge: | Existing models are primarily optimized for task-specific performance, lacking well-defined objectives or linguistic grounding. |
| Approach: | They propose a token-to-word decoding algorithm that extends theoretically grounded probability estimation to open-vocabulary settings. |
| Outcome: | The proposed algorithm can localize sentence-level inconsistency at the word level, establishing a quantitative link between lexical uncertainty and sentential semantics. |
Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language (2025.coling-main)
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Jai Riley, Bradley M. Hauer, Nafisa Sadaf Hriti, Guoqing Luo, Amir Reza Mirzaei, Ali Rafiei, Hadi Sheikhi, Mahvash Siavashpour, Mohammad Tavakoli, Ning Shi, Grzegorz Kondrak
| Challenge: | Word sense disambiguation is a widely studied NLP task of identifying the meaning of a word in context. |
| Approach: | They propose a method to create parallel sense-annotated datasets in English . they use machine translation, word alignment, sense projection, and sense filtering to produce silver annotations . |
| Outcome: | The proposed method produces parallel sense-annotated datasets on Farsi, Chinese, and Bengali . the results are higher than those obtained with recent multilingual systems, the authors say . |
Taxonomy of Problems in Lexical Semantics (2023.findings-acl)
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| Challenge: | Semantic tasks are rarely formally defined, and the exact relationship between them is unclear . a taxonomy of several problems in lexical semantics is proposed to clarify this . |
| Approach: | They propose a taxonomy that elucidates the connection between several problems in lexical semantics . they propose equivalence theory and algorithmic problem reductions to reduce problems to word sense disambiguation (WSD) |
| Outcome: | The proposed taxonomy proves that word sense disambiguation and word synonymy are theoretically equivalent. |
Semantically-Prompted Language Models Improve Visual Descriptions (2024.findings-naacl)
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| Challenge: | Language-vision models have made significant progress in zeroshot vision tasks, but lack expressive visual descriptions. |
| Approach: | They propose a new method for generating visual descriptions with pre-trained language models and semantic knowledge bases. |
| Outcome: | The proposed method improves visual descriptions and achieves strong results on image-classification datasets. |
Grounding the Lexical Substitution Task in Entailment (2023.findings-acl)
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| Challenge: | Existing definitions of lexical substitutes are vague or inconsistent with the gold annotations. |
| Approach: | They propose a new definition which is grounded in the relation of entailment . they empirically validate the definition and create a dataset from existing semantic resources . |
| Outcome: | The proposed method improves the performance of existing lexical substitution systems on the existing benchmarks. |
Paraphrase Identification via Textual Inference (2024.starsem-1)
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| Challenge: | Paraphrase identification (PI) and natural language inference (NLI) are important tasks in natural language processing. |
| Approach: | They propose a method for paraphrase identification and natural language inference using an NLI system to solve these tasks. |
| Outcome: | The proposed method outperforms dedicated PI models on PI datasets and provides insights into limitations of current benchmarks. |
Don’t Trust ChatGPT when your Question is not in English: A Study of Multilingual Abilities and Types of LLMs (2023.emnlp-main)
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| Challenge: | Existing studies have shown that large language models can perform a wide variety of language tasks when presented in English. |
| Approach: | They propose a method to evaluate the multilingual capabilities of large language models using a prompt back-translation method to find out how LLMs acquire their multilingual abilities. |
| Outcome: | The proposed method shows that large language models can transfer learned knowledge across different languages, but struggle to provide accurate results in translation-variant tasks. |
Lexical Substitution as Causal Language Modeling (2024.starsem-1)
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| Challenge: | Existing methods for lexical substitution task lacks autoregressive decoding capabilities. |
| Approach: | They propose a framework that uses causal language modeling (CLM) for lexical substitution task. |
| Outcome: | The proposed system outperforms GeneSis, the best previously published supervised LST method. |
Bridging the Gap Between BabelNet and HowNet: Unsupervised Sense Alignment and Sememe Prediction (2023.eacl-main)
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| Challenge: | Sememes are the minimum semantic units of natural languages, but their use is limited by a lack of available sememe knowledge bases. |
| Approach: | They propose to use sense alignment to connect BabelNet with HowNet by relaxing constraints until a complete alignment is achieved. |
| Outcome: | The proposed method improves on previous supervised methods by 12% . it is based on interpretable propagation of sememe information between lexical resources . |
Improving Word Sense Disambiguation with Translations (2020.emnlp-main)
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| Challenge: | Existing WSD systems rarely consider multilingual information for word sense disambiguation (WSD). |
| Approach: | They propose a method that leverages multilingual information to improve a base WSD system by generating translations. |
| Outcome: | The proposed method improves performance of a base WSD system in English and multilingual WSD on several languages. |
Identifying Emotional and Polar Concepts via Synset Translation (2024.starsem-1)
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| Challenge: | Emotion identification and polarity classification seek to determine sentiment expressed by a writer. |
| Approach: | They propose a translation-based method for labeling each individual word sense and lexical concept into 20 different languages and translate them into multilingual sentiment lexicons. |
| Outcome: | The proposed method outperforms existing methods and is available on GitHub . it contains 12,429 emotional synsets and 15,567 polar synset. |
WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks (2022.naacl-main)
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| Challenge: | Word-in-Context (WiC) task has attracted considerable attention in the NLP community, as demonstrated by the popularity of the recent MCL-Wic SemEval shared task. |
| Approach: | They propose to use lexical resources from word sense disambiguation and target sense verification to reduce the relationship between the two tasks. |
| Outcome: | The proposed methods can be pairwise reduced to each other and therefore work in practice. |
Improving HowNet-Based Chinese Word Sense Disambiguation with Translations (2022.findings-emnlp)
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| Challenge: | Prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, but these have proven to be less effective for Chinese. |
| Approach: | They propose a system which combines contextual information from a pretrained neural language model with bilingual information obtained via machine translation and sense translation information from HowNet. |
| Outcome: | The proposed system achieves a state-of-the-art for unsupervised Chinese WSD. |
Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms (2024.acl-long)
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| Challenge: | Existing methods for identifying lexical gaps have been limited . kinship terms are well-suited for investigations into lexicons and lexicals . |
| Approach: | They propose an algorithm to automatically generate concept lexicalizations based on machine translation and hypernymy relations between concepts. |
| Outcome: | Empirical evaluations show that the proposed method is more accurate than BabelNet and ChatGPT. |