| Challenge: | a recent paper examines the relationship between semantics and pragmatics in language. |
| Approach: | They propose to develop computational models that leverage pragmatic knowledge in language . goal is to build better and more pragmatically-aware natural language generation and understanding systems . |
| Outcome: | The proposed models leverage pragmatic knowledge in language crucial to performing many NLP tasks correctly. |
Similar Papers
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
| Approach: | They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications. |
| Outcome: | The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models . |
Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches (2023.findings-emnlp)
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| Challenge: | People rely heavily on context to enrich meaning beyond what is literally said. |
| Approach: | They analyze how task goals, environmental contexts, and communicative affordances in each work enrich linguistic meaning. |
| Outcome: | The proposed frameworks are based on linguistic goals, environmental contexts, and communicative affordances to enrich linguistic meaning. |
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2022.tacl-1)
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Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
| Challenge: | causality has not had the same importance in natural language processing, says aaron e. smith . he says research on causality in NLP remains scattered across domains without unified definitions . |
| Approach: | They propose to consolidate research on causality in NLP across academic areas . they explore potential uses of causal inference to improve robustness, fairness, interpretability . |
| Outcome: | The proposed method is a unified overview of causal inference for the NLP community. |
Computational modeling of semantic change (2024.eacl-tutorials)
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| Challenge: | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. |
| Approach: | They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks. |
| Outcome: | In historical linguistics, tools and methods have been developed to analyse the process . they include categorisations of types of change, causes and mechanisms . but traditional methods, while informative, are often based on small, carefully curated samples. |
Pragmatically Informative Text Generation (N19-1)
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| Challenge: | Existing approaches to pragmatics have been used to improve the informativeness of generated text in grounded language learning problems. |
| Approach: | They propose to use pragmatics to improve the informativeness of conditional text models . they propose to apply pragmatic reasoning to more traditional language generation tasks . |
| Outcome: | The proposed methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations. |
Context Matters: A Pragmatic Study of PLMs’ Negation Understanding (2022.acl-long)
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| Challenge: | In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. |
| Approach: | They propose to use transformer-based pre-trained language models to study negation understanding using a pragmatic paradigm. |
| Outcome: | The proposed transformer-based model outperforms the human benchmark at NLU and GLUE, and the results are much more optimistic than previous studies. |
Semantic Accuracy in Natural Language Generation: A Thesis Proposal (2023.acl-srw)
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| Challenge: | Using large pre-trained language models, it is essential to research their reliability . if a human does not know the answer to a question, the socially acceptable behavior is to say 'I do not know' failing to fulfill this expectation can lead to distrust, or spread of misinformation. |
| Approach: | They propose a method for evaluating semantic accuracy and a benchmark for NLG metrics. |
| Outcome: | The proposed method evaluates semantic accuracy and provides a benchmark for NLG metrics. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
A Pragmatics-Centered Evaluation Framework for Natural Language Understanding (2022.lrec-1)
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| Challenge: | a number of studies have suggested that models induce universal text representations . current benchmarks focus on semantic phenomena, so pragmatics needs to be the focus . |
| Approach: | They propose a benchmark that unites 11 pragmatics-focused evaluation datasets for English. |
| Outcome: | The proposed benchmark shows that natural language inference does not result in genuinely universal representations. |
The Interplay between Metaphors and NLP (2026.acl-tutorials)
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| Challenge: | This tutorial will provide an overview of the metaphor processing field. |
| Approach: | This tutorial will provide an overview of the metaphor processing field . it will focus on recent directions opened by LLMs for metaphor interpretation . |
| Outcome: | The tutorial will discuss the influence of various metaphor theories on the creation of annotated resources and models. |