Papers with lexical entailment
Scoring Lexical Entailment with a Supervised Directional Similarity Network (P18-2)
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| Challenge: | Existing word embeddings that use supervision only improve the embeddable word embeds of words with annotated lexical relations. |
| Approach: | They propose a supervised directional similarity network for learning task-specific transformation functions on top of general-purpose word embeddings. |
| Outcome: | The proposed model outperforms existing models on the HyperLex dataset on a directional graded lexical entailment task by 25%. |
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks (2023.eacl-main)
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| Challenge: | Recent work on word embeddings reports low correlations with human ratings . contextual language models (CLMs) have been successful in acquiring semantic and world knowledge. |
| Approach: | They propose to use BERT to probe contextual language models for predicting typicality scores. |
| Outcome: | The proposed methods improve on previous studies on word embeddings and their ability to predict typicality scores. |
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. |
Mining Knowledge for Natural Language Inference from Wikipedia Categories (2020.findings-emnlp)
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| Challenge: | Accurate lexical entailment (LE) and natural language inference (NLI) tasks require expensive annotations. |
| Approach: | They propose to pretrain Wikipedia categories for lexical entailment and natural language inference by pretraining them on WikiNLI and transferring them to other knowledge bases. |
| Outcome: | The proposed model can improve strong baselines such as BERT and RoBERTa by pretraining on WikiNLI and transferring the models on downstream tasks. |
Hypernymy Detection for Low-Resource Languages via Meta Learning (2020.acl-main)
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| Challenge: | Existing studies focus on monolingual hypernymy detection on high-resource languages, but few investigate low-resourced scenarios. |
| Approach: | They propose to combine high-resource languages to solve low-resourced hypernymy detection problem . they extensively compare three joint training paradigms and propose meta learning . |
| Outcome: | The proposed method significantly improves performance of extremely low-resource languages by preventing over-fitting on small datasets. |
Talking the Talk Does Not Entail Walking the Walk: On the Limits of Large Language Models in Lexical Entailment Recognition (2024.findings-emnlp)
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| Challenge: | Verbs are crucial for expressing actions and relationships between entities, making it essential to properly capture their nuances. |
| Approach: | They propose to use prompting strategies and zero-shot prompting to recognize entailment relations among verbs from two lexical databases, WordNet and HyperLex. |
| Outcome: | The proposed models can tackle the lexical entailment recognition task with moderately good performance, although at varying degree of effectiveness and under different conditions. |