| Challenge: | Textual information extraction (IE) uses textual features to negate stronger statements, such as the negation of stronger statements. |
| Approach: | They propose to use textual features to predict whether a given text segment mentions all objects standing in a certain relationship with a subject. |
| Outcome: | The proposed features can predict whether a given text segment mentions all objects standing in a certain relationship with a particular subject. |
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Pragmatic inference of scalar implicature by LLMs (2024.acl-srw)
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| Challenge: | Existing Large Language Models (LLMs) engage in pragmatic inference of scalar implicature, such as some. |
| Approach: | They investigate how Large Language Models (LLMs) engage in pragmatic inference of scalar implicature, such as some. |
| Outcome: | The proposed models interpret some as pragmatic implicature not all in the absence of context, aligning with human language processing. |
SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives (2024.lrec-main)
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| Challenge: | scalar implicatures are a phenomenon by which a speaker conveys the negation of a more informative utterance by producing a less informative . |
| Approach: | They propose to use a dataset to investigate the ability of language models to interpret utterances with scalar implicatures. |
| Outcome: | The proposed models perform significantly worse on in-domain and out-of-domain examples than other types of NLI examples. |
Predicting Document Coverage for Relation Extraction (2022.tacl-1)
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| Challenge: | Existing methods for predicting document coverage for relation extraction (RE) are limited in their predictive power. |
| Approach: | They propose a task of predicting the coverage of a text document for relation extraction . they analyze a dataset of 31,366 diverse documents for 520 entities . |
| Outcome: | The proposed model achieves an F1 score of up to 46% on two use cases. |
Harnessing the linguistic signal to predict scalar inferences (2020.acl-main)
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| Challenge: | Recent Bayesian game-theoretic models of pragmatic reasoning can predict the strength of scalar inferences by using linguistic features. |
| Approach: | They propose to use a sentence encoder to predict the strength of scalar inferences by using a corpus of linguistic data. |
| Outcome: | The proposed model infers previously established associations between linguistic features and inference strength, suggesting that it learns to use linguistic feature to predict pragmatic inferences. |
Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics (2024.lrec-main)
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| Challenge: | Scalar adjectives describe different domain scales and vary in intensity . they can be triggered by scalar adjective and require listeners to reason pragmatically about them. |
| Approach: | They probe different families of Large Language Models for their knowledge of the lexical semantics of scalar adjectives and one specific aspect of their pragmatics. |
| Outcome: | The proposed models encode rich lexical-semantic information about scalar adjectives but lack a good understanding of skalar diversity. |
Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
| Approach: | They propose a framework for interpretable text embeddings and text similarity explanation . they characterize the main ideas, approaches, and trade-offs and discuss lessons learned . |
| Outcome: | The proposed methods are compared with existing models and compare them with existing ones. |
Understanding Conversational Implicatures in Humans and LLMs (2026.acl-srw)
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| Challenge: | Large Language Models (LLMs) interpret conversational implicatures using humans as a baseline . et al.: do LLMs exhibit a human-like sensitivity to pragmatic inference? |
| Approach: | They adopt a surprisal-based and response-based metric to measure the accuracy of implicatures . they find that LLMs employing the response- based meter exhibit human-like behavior . |
| Outcome: | The proposed model performs better in the literal condition than in the implied condition . the model differs from humans in its understanding of conversational implicatures . |
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
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| Challenge: | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a document. |
| Approach: | They propose a document-level relation extraction framework that captures and exploits instructive information by adding extra syntactic information into text representations. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
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. |
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. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |