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. |
| Outcome: | Experiments show that the pretrained DEISTE on SciTail gets 5% improvement over prior state of the art and that it generalizes well on RTE-5. |
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Bridging Knowledge Gaps in Neural Entailment via Symbolic Models (D18-1)
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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. |
It is not a piece of cake for GPT: Explaining Textual Entailment Recognition in the presence of Figurative Language (2025.coling-main)
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| Challenge: | Figure-based language is used to convey opinions, ideas, or emotions in texts and dialogues. |
| Approach: | They evaluate the capabilities of Large Language Models to address TER and generate textual explanations of TER predictions. |
| Outcome: | The proposed model outperforms the open-source models in Zero- and Few-Shot Learning settings and shows significant performance improvements. |
Explaining Answers with Entailment Trees (2021.emnlp-main)
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Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, Peter Clark
| Challenge: | ENTAILMENTBANK is the first dataset to contain multistep entailment trees. |
| Approach: | They propose to generate explanations in the form of entailment trees, a tree of multipremise entanglements steps from facts that are known to the hypothesis of interest. |
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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. |
| Outcome: | The proposed methods increase accuracy by 4.7% on SciTail and 2.8% on a 1% sub-sample of SNLI. |
Natural Language Deduction through Search over Statement Compositions (2022.findings-emnlp)
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| Challenge: | Existing methods focus on the end-to-end discriminative version of this task, but less work has treated the generative version of the task. |
| Approach: | They propose a system that decomposes the task into separate steps coordinated by a search procedure and produces a tree of intermediate conclusions that faithfully reflects the system’s reasoning process. |
| Outcome: | The proposed system proves true statements while rejecting false ones on the EntailmentBank dataset with a 17% absolute higher step validity than the end-to-end T5 model. |
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (P18-1)
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| Challenge: | Recent advances on abstractive summarization have allowed substantial improvements in the quality of the model, but there is still scope for improvement. |
| Approach: | They propose novel multi-task architectures with high-level layer-specific sharing across multiple encoder and decoder layers of the three tasks and soft-sharing mechanisms. |
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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 . |
| Outcome: | The proposed framework improves retrieval capabilities, understanding relationships and generating intermediate conclusions. |
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
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| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
| Approach: | They propose 10 probing tasks designed to capture simple linguistic features of sentences . they use three different encoders to train embeddings in eight different ways . |
| Outcome: | The proposed tasks capture key linguistic features of sentences, but they are difficult to infer from them. |
Embedding WordNet Knowledge for Textual Entailment (C18-1)
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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. |
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition (2023.findings-acl)
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| Challenge: | Existing systems for Natural Language Inference (NLI) only recognize textual entailment relations on sentence-level . however, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence . |
| Approach: | They propose a system to recognize whether one text is textually entailed by another . they use a corpus of over 45K propositions annotated by human raters to study the textual entailment relation of each proposition in a sentence individually. |
| Outcome: | The proposed dataset can be used to understand the compositionality of NLI labels. |