| 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. |
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| 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. |
Empowering Tree-structured Entailment Reasoning: Rhetorical Perception and LLM-driven Interpretability (2024.lrec-main)
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| Challenge: | Existing models for science question answering lack a framework for entailment trees . ambiguities and similarities between science facts complicate the fact retrieval process . |
| Approach: | They propose a framework for building entailment trees for science question answering . they propose to infuse knowledge that bridges the gap between reasoning types and rhetorical relations . |
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Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
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| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
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Neural Natural Logic Inference for Interpretable Question Answering (2021.emnlp-main)
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| Challenge: | Existing question answering models are based on textual entailment tasks . prior work has focused on QA on premise-based questions . |
| Approach: | They propose a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures towards developing effective question answering models. |
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End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions (P18-2)
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| Challenge: | Existing datasets for textual entailment (TE) have been used to study TE. |
| Approach: | They propose a deep explorations of inter-sentence interactions for textual entailment task that uses a convolution to make important words in P and H play a dominant role in learnt representations. |
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How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
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Entailed Between the Lines: Incorporating Implication into NLI (2025.acl-long)
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Shreya Havaldar, Hamidreza Alvari, John Palowitch, Mohammad Javad Hosseini, Senaka Buthpitiya, Alex Fabrikant
| Challenge: | True Emotions, social cues, insults, and a myriad of other messages are conveyed implicitly, often even more so than explicitly. |
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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. |
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Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules (2022.emnlp-main)
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Prithviraj Sen, Breno William Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Salim Roukos, Alexander Gray
| Challenge: | Knowledge base completion (KBC) is a human-interpretable dialect . rule-based KBC has a high quality but low accuracy . |
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Lexicosyntactic Inference in Neural Models (D18-1)
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| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
| Approach: | They build a factuality judgment dataset for English clause-embedding verbs in various syntactic contexts and use it to probe the behavior of current state-of-the-art neural systems. |
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