| Challenge: | Existing deep learning models for textual entailment do not require any feature engineering or linguistic analysis. |
| Approach: | They propose to embed WordNet-derived lexical entailment relations into specially-learned word vectors and incorporate them into a decomposable attention model for textual enlightment. |
| Outcome: | The proposed model significantly improves on the SICK and SNLI datasets. |
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| Challenge: | Textual entailment models focus on lexical gaps but rarely on knowledge gaps. |
| Approach: | They propose a fact-level decomposition of the hypothesis and a knowledge lookup module to fill knowledge gaps in Science Entailment task. |
| Outcome: | The proposed model outperforms the base model on the SciTail dataset by 3% and 5% on the textual premise and the structured knowledge base. |
WordNet Is All You Need: A Surprisingly Effective Unsupervised Method for Graded Lexical Entailment (2023.findings-emnlp)
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| Challenge: | a simple unsupervised method for predicting graded lexical entailment in English relies on WordNet . despite its simplicity, our method outperforms all previous methods using WordNet as weak supervision. |
| Approach: | They propose an unsupervised method which relies exclusively on WordNet for predicting graded lexical entailment in English. |
| Outcome: | The proposed method outperforms existing methods on the largest GLE dataset using WordNet. |
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. |
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Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources (2020.acl-srw)
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| Challenge: | In this paper, we present an effective method for semantic specialization of word vector representations. |
| Approach: | They propose a method for semantic specialization of word vector representations using BabelNet. |
| Outcome: | The proposed method improves on word similarity and dialog state tracking tasks. |
Lexical Entailment with Hierarchy Representations by Deep Metric Learning (2022.findings-emnlp)
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| Challenge: | Existing lexical entailment studies cannot be applied to words that are not included in the training dataset. |
| Approach: | They propose a method that learns a mapping from word embeddings to hierarchical embedds to predict hypernymy relations among words. |
| Outcome: | The proposed method achieves state-of-the-art performance and robustness for unknown words. |
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (P18-1)
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| Challenge: | Recent deep learning entailment systems have achieved close to human level performance on large datasets, but the problem is far from solved. |
| Approach: | They propose a knowledge-guided adversarial example generator for incorporating large lexical resources into entailment models via only a handful of rule templates and a natural language example generator that iteratively adjusts to the discriminator’s weaknesses. |
| Outcome: | The proposed methods increase accuracy by 4.7% on SciTail and 2.8% on a 1% sub-sample of SNLI. |
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)
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| Challenge: | Contextual embeddings address the problem of meaning conflation hampering word embeddables. |
| Approach: | They propose a method that creates sense-level embeddings with full-coverage of WordNet without recourse to explicit sense distributions or task-specific modelling. |
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Global Textual Relation Embedding for Relational Understanding (P19-1)
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| Challenge: | Existing methods to learn textual relation embeddings are lacking in large open-domain corpora. |
| Approach: | They propose to learn a general-purpose embedding of textual relations using a large dataset from Freebase. |
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A Corpus to Learn Refer-to-as Relations for Nominals (L18-1)
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| Challenge: | Existing work on how to learn refer-to-as relations from large unlabeled corpora lacks coreferential information. |
| Approach: | They propose to use Wikipedia to generate coreferential neural embeddings for nominals . they use coreference resolution as a proxy to evaluate the neural embeds for noun phrases . |
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Word Attribute Prediction Enhanced by Lexical Entailment Tasks (2020.lrec-1)
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| Challenge: | a semantic attribute is associated with a designated dimension in attribute-based vector representations . semantic attributes are created by psychological experimental settings involving human annotators . a conceptual attribute of a concept dictates a specific semantic aspect of the concept . |
| Approach: | They propose a two-stage neural network architecture that fine-tunes attribute representations by employing supervised entailment tasks. |
| Outcome: | The proposed method improves performance of semantic/visual similarity/relatedness evaluation tasks. |