A Taxonomy of Ambiguity Types for NLP

• URL: http://arxiv.org/abs/2403.14072v1
• Date: Thu, 21 Mar 2024 01:47:22 GMT
• Title: A Taxonomy of Ambiguity Types for NLP
• Authors: Margaret Y. Li, Alisa Liu, Zhaofeng Wu, Noah A. Smith,
• Abstract summary: 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.
• Score: 53.10379645698917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP. Recent work suggests that NLP systems may struggle to grasp certain elements of human language understanding because they may not handle ambiguities at the level that humans naturally do in communication. Additionally, different types of ambiguity may serve different purposes and require different approaches for resolution, and we aim to investigate how language models' abilities vary across types. 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.

Related papers

• Exploring transfer learning for Deep NLP systems on rarely annotated languages [0.0]
  This thesis investigates the application of transfer learning for Part-of-Speech (POS) tagging between Hindi and Nepali. We assess whether multitask learning in Hindi, with auxiliary tasks such as gender and singular/plural tagging, can contribute to improved POS tagging accuracy.
  arXiv  Detail & Related papers  (2024-10-15T13:33:54Z)
• Towards Human Understanding of Paraphrase Types in ChatGPT [7.662751948664846]
  Atomic paraphrase types (APT) decompose paraphrases into different linguistic changes. We introduce APTY (Atomic Paraphrase TYpes), a dataset of 500 sentence-level and word-level annotations by 15 annotators. Our results reveal that ChatGPT can generate simple APTs, but struggle with complex structures.
  arXiv  Detail & Related papers  (2024-07-02T14:35:10Z)
• 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)
• Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining? [34.609984453754656]
  We aim to elucidate the impact of comprehensive linguistic knowledge, including semantic expression and syntactic structure, on multimodal alignment. Specifically, we design and release the SNARE, the first large-scale multimodal alignment probing benchmark.
  arXiv  Detail & Related papers  (2023-08-24T16:17:40Z)
• We're Afraid Language Models Aren't Modeling Ambiguity [136.8068419824318]
  Managing ambiguity is a key part of human language understanding. We characterize ambiguity in a sentence by its effect on entailment relations with another sentence. We show that a multilabel NLI model can flag political claims in the wild that are misleading due to ambiguity.
  arXiv  Detail & Related papers  (2023-04-27T17:57:58Z)
• Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
  Figurative and metaphorical language are commonplace in discourse. It remains an open question to what extent modern language models can interpret nonliteral phrases. We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
  arXiv  Detail & Related papers  (2022-04-26T23:42:22Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• Infusing Finetuning with Semantic Dependencies [62.37697048781823]
  We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
  arXiv  Detail & Related papers  (2020-12-10T01:27:24Z)

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.