Natural Language Processing for Dialects of a Language: A Survey

• URL: http://arxiv.org/abs/2401.05632v4
• Date: Fri, 06 Dec 2024 23:14:39 GMT
• Title: Natural Language Processing for Dialects of a Language: A Survey
• Authors: Aditya Joshi, Raj Dabre, Diptesh Kanojia, Zhuang Li, Haolan Zhan, Gholamreza Haffari, Doris Dippold,
• Abstract summary: 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 dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
• Score: 56.93337350526933
• License:
• Abstract: 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 dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches. We describe a wide range of NLP tasks in terms of two categories: natural language understanding (NLU) (for tasks such as dialect classification, sentiment analysis, parsing, and NLU benchmarks) and natural language generation (NLG) (for summarisation, machine translation, and dialogue systems). The survey is also broad in its coverage of languages which include English, Arabic, German, among others. We observe that past work in NLP concerning dialects goes deeper than mere dialect classification, and extends to several NLU and NLG tasks. For these tasks, we describe classical machine learning using statistical models, along with the recent deep learning-based approaches based on pre-trained language models. We expect that this survey will be useful to NLP researchers interested in building equitable language technologies by rethinking LLM benchmarks and model architectures.

Related papers

• Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks [0.0]
  We introduce eight new datasets, creating a new benchmark, the Nepali Language Understanding Evaluation (NLUE) benchmark. The benchmark covers a total of 12 tasks for evaluating the performance of models across a diverse set of Natural Language Understanding (NLU) tasks. On evaluating the models using added tasks, we observe that the existing models fall short in handling complex NLU tasks effectively.
  arXiv  Detail & Related papers  (2024-11-28T16:32:02Z)
• DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages [49.38663048447942]
  We propose DIALECTBENCH, the first-ever large-scale benchmark for NLP on varieties. This allows for a comprehensive evaluation of NLP system performance on different language varieties. We provide substantial evidence of performance disparities between standard and non-standard language varieties.
  arXiv  Detail & Related papers  (2024-03-16T20:18:36Z)
• 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)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training [0.34998703934432673]
  This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP) To overcome this limitation, we create a dedicated data set from publicly available resources. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches.
  arXiv  Detail & Related papers  (2023-07-27T07:40:11Z)
• FedNLP: A Research Platform for Federated Learning in Natural Language Processing [55.01246123092445]
  We present the FedNLP, a research platform for federated learning in NLP. FedNLP supports various popular task formulations in NLP such as text classification, sequence tagging, question answering, seq2seq generation, and language modeling. Preliminary experiments with FedNLP reveal that there exists a large performance gap between learning on decentralized and centralized datasets.
  arXiv  Detail & Related papers  (2021-04-18T11:04:49Z)
• Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios [48.57072884674938]
  We propose a method to analyze language similarity using deep learning. Namely, we train a model on the Wilderness dataset and investigate how its latent space compares with classical language family findings.
  arXiv  Detail & Related papers  (2020-12-01T22:44:42Z)
• Low-Resource Adaptation of Neural NLP Models [0.30458514384586405]
  This thesis investigates methods for dealing with low-resource scenarios in information extraction and natural language understanding. We develop and adapt neural NLP models to explore a number of research questions concerning NLP tasks with minimal or no training data.
  arXiv  Detail & Related papers  (2020-11-09T12:13:55Z)

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.