| Challenge: | Existing methods for Recognizing Textual Entailment (RTE) lack phrasal knowledge. |
| Approach: | They propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. |
| Outcome: | The proposed method detects paraphrases that are absent from existing paraphrase databases and improves accuracy of RTE tasks. |
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| Challenge: | Existing paraphrase identification datasets exhibit high correlation between positive pairs and the degree of their lexical overlap. |
| Approach: | They propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans and decomposing the sentence-level meaning comparison into the alignment between their spans. |
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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. |
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Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)
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| Challenge: | a new adversarial method of paraphrase identification is being used to identify paraphrases based on word overlap and syntax . authors propose a dataset that generates semantically equivalent but lexically and syntactically disparate paraphrase pairs . |
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Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity (2020.lrec-1)
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| Challenge: | SHARel is a new typology for decomposing and comparing multiple meaning relations . it consists of 26 linguistic and 8 reason-based categories and can be applied to all relations with a high inter-annotator agreement. |
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Cross-lingual Inference with A Chinese Entailment Graph (2022.findings-acl)
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| Challenge: | Existing work on predicate entailment detection from typed open relation triples has not been able to detect predicates. |
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Essentia: Mining Domain-specific Paraphrases with Word-Alignment Graphs (D19-53)
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| Challenge: | Existing methods for mining general-purpose paraphrases are often based on statistical methods, but domain-specific corpora are too small to fit statistical methods. |
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Predicate-Argument Based Bi-Encoder for Paraphrase Identification (2022.acl-long)
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| Challenge: | Cross-encoders have been widely applied to sentence pair tasks . bi-encodings have significantly lower computation complexity and are better suited to symmetric tasks. |
| Approach: | They propose a bi-encoder approach to the paraphrase identification task and incorporate predicate-argument information into SBERT. |
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Evaluating Paraphrastic Robustness in Textual Entailment Models (2023.acl-short)
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| Challenge: | Recognizing Textual Entailment models understand language and should be robust to paraphrases. |
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Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
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Entailment Graph Learning with Textual Entailment and Soft Transitivity (2022.acl-long)
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| Challenge: | Typed entailment graphs suffer from severe sparsity and unreliability of distributional similarity . enlargement relation is critical to semantic understanding and natural language inference . |
| Approach: | They propose a method to learn local entailment relations by recognizing textual enanglement between template sentences formed by typed CCG-parsed predicates. |
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