Natural Language Processing RELIES on Linguistics

• URL: http://arxiv.org/abs/2405.05966v3
• Date: Fri, 22 Nov 2024 15:36:32 GMT
• Title: Natural Language Processing RELIES on Linguistics
• Authors: Juri Opitz, Shira Wein, Nathan Schneider,
• Abstract summary: We argue the acronym RELIES that encapsulates six major facets where linguistics contributes to NLP. This list is not exhaustive, nor is linguistics the main point of reference for every effort under these themes.
• Score: 13.142686158720021
• License:
• Abstract: Large Language Models (LLMs) have become capable of generating highly fluent text in certain languages, without modules specially designed to capture grammar or semantic coherence. What does this mean for the future of linguistic expertise in NLP? We highlight several aspects in which NLP (still) relies on linguistics, or where linguistic thinking can illuminate new directions. We argue our case around the acronym RELIES that encapsulates six major facets where linguistics contributes to NLP: Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and the Study of language. This list is not exhaustive, nor is linguistics the main point of reference for every effort under these themes; but at a macro level, these facets highlight the enduring importance of studying machine systems vis-\`a-vis systems of human language.

Related papers

• Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
  Lens is a novel approach to enhance multilingual capabilities of large language models (LLMs) It operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. It achieves superior results with much fewer computational resources compared to existing post-training approaches.
  arXiv  Detail & Related papers  (2024-10-06T08:51:30Z)
• Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency [0.11510009152620666]
  We argue that claims regarding linguistic capabilities of Large Language Models (LLMs) are based on at least two unfounded assumptions. Language completeness assumes that a distinct and complete thing such as a natural language' exists. The assumption of data completeness relies on the belief that a language can be quantified and wholly captured by data.
  arXiv  Detail & Related papers  (2024-07-11T18:06:01Z)
• A Taxonomy of Ambiguity Types for NLP [53.10379645698917]
  We propose a taxonomy of ambiguity types as seen in English to facilitate NLP analysis. Our taxonomy can help make meaningful splits in language ambiguity data, allowing for more fine-grained assessments of both datasets and model performance.
  arXiv  Detail & Related papers  (2024-03-21T01:47:22Z)
• What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects [60.8361859783634]
  We survey speakers of dialects and regional languages related to German. We find that respondents are especially in favour of potential NLP tools that work with dialectal input.
  arXiv  Detail & Related papers  (2024-02-19T09:15:28Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• Ling-CL: Understanding NLP Models through Linguistic Curricula [17.44112549879293]
  We employ a characterization of linguistic complexity from psycholinguistic and language acquisition research. We develop data-driven curricula to understand the underlying linguistic knowledge that models learn to address NLP tasks.
  arXiv  Detail & Related papers  (2023-10-31T01:44:33Z)
• Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across Languages [3.716965622352967]
  We propose new criteria to evaluate the quality of lexical representation and vocabulary overlap observed in sub-word tokenizers. Our findings show that the overlap of vocabulary across languages can be actually detrimental to certain downstream tasks.
  arXiv  Detail & Related papers  (2023-05-26T18:06:49Z)
• CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
  We release a Corpus of Linguistically Significant Entities (CLSE) annotated by experts. CLSE covers 74 different semantic types to support various applications from airline ticketing to video games. We create a linguistically representative NLG evaluation benchmark in three languages: French, Marathi, and Russian.
  arXiv  Detail & Related papers  (2022-11-04T12:56:12Z)
• Including Signed Languages in Natural Language Processing [48.62744923724317]
  Signed languages are the primary means of communication for many deaf and hard of hearing individuals. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact.
  arXiv  Detail & Related papers  (2021-05-11T17:37:55Z)
• The State and Fate of Linguistic Diversity and Inclusion in the NLP World [12.936270946393483]
  Language technologies contribute to promoting multilingualism and linguistic diversity around the world. Only a very small number of the over 7000 languages of the world are represented in the rapidly evolving language technologies and applications.
  arXiv  Detail & Related papers  (2020-04-20T07:19:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.