Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks

• URL: http://arxiv.org/abs/2411.19244v1
• Date: Thu, 28 Nov 2024 16:32:02 GMT
• Title: Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks
• Authors: Jinu Nyachhyon, Mridul Sharma, Prajwal Thapa, Bal Krishna Bal,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: The Nepali language has distinct linguistic features, especially its complex script (Devanagari script), morphology, and various dialects, which pose a unique challenge for natural language processing (NLP) evaluation. While the Nepali Language Understanding Evaluation (Nep-gLUE) benchmark provides a foundation for evaluating models, it remains limited in scope, covering four tasks. This restricts their utility for comprehensive assessments of NLP models. To address this limitation, we introduce eight new datasets, creating a new benchmark, the Nepali Language Understanding Evaluation (NLUE) benchmark, which covers a total of 12 tasks for evaluating the performance of models across a diverse set of Natural Language Understanding (NLU) tasks. The added tasks include single-sentence classification, similarity and paraphrase tasks, and Natural Language Inference (NLI) tasks. On evaluating the models using added tasks, we observe that the existing models fall short in handling complex NLU tasks effectively. This expanded benchmark sets a new standard for evaluating, comparing, and advancing models, contributing significantly to the broader goal of advancing NLP research for low-resource languages.

Related papers

• One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks [26.848664285007022]
  ByT5-Sanskrit is designed for NLP applications involving the morphologically rich language Sanskrit. It is easier to deploy and more robust to data not covered by external linguistic resources. We show that our approach yields new best scores for lemmatization and dependency parsing of other morphologically rich languages.
  arXiv  Detail & Related papers  (2024-09-20T22:02:26Z)
• EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation [24.060772057458685]
  This paper introduces EthioLLM -- multilingual large language models for five Ethiopian languages (Amharic, Ge'ez, Afan Oromo, Somali, and Tigrinya) and English. We evaluate the performance of these models across five downstream Natural Language Processing (NLP) tasks.
  arXiv  Detail & Related papers  (2024-03-20T16:43:42Z)
• 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)
• 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 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.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• SOUL: Towards Sentiment and Opinion Understanding of Language [96.74878032417054]
  We propose a new task called Sentiment and Opinion Understanding of Language (SOUL) SOUL aims to evaluate sentiment understanding through two subtasks: Review (RC) and Justification Generation (JG)
  arXiv  Detail & Related papers  (2023-10-27T06:48:48Z)
• WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation [101.00109827301235]
  We introduce a novel paradigm for dataset creation based on human and machine collaboration. We use dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instruct GPT-3 to compose new examples with similar patterns. The resulting dataset, WANLI, consists of 108,357 natural language inference (NLI) examples that present unique empirical strengths.
  arXiv  Detail & Related papers  (2022-01-16T03:13:49Z)
• Predicting the Performance of Multilingual NLP Models [16.250791929966685]
  This paper proposes an alternate solution for evaluating a model across languages which make use of the existing performance scores of the model on languages that a particular task has test sets for. We train a predictor on these performance scores and use this predictor to predict the model's performance in different evaluation settings. Our results show that our method is effective in filling the gaps in the evaluation for an existing set of languages, but might require additional improvements if we want it to generalize to unseen languages.
  arXiv  Detail & Related papers  (2021-10-17T17:36:53Z)
• e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks [52.918087305406296]
  We introduce e-ViL, a benchmark for evaluate explainable vision-language tasks. We also introduce e-SNLI-VE, the largest existing dataset with NLEs. We propose a new model that combines UNITER, which learns joint embeddings of images and text, and GPT-2, a pre-trained language model.
  arXiv  Detail & Related papers  (2021-05-08T18:46:33Z)
• KLEJ: Comprehensive Benchmark for Polish Language Understanding [4.702729080310267]
  We introduce a comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard. We also release HerBERT, a Transformer-based model trained specifically for the Polish language, which has the best average performance and obtains the best results for three out of nine tasks.
  arXiv  Detail & Related papers  (2020-05-01T21:55:40Z)
• Coreferential Reasoning Learning for Language Representation [88.14248323659267]
  We present CorefBERT, a novel language representation model that can capture the coreferential relations in context. The experimental results show that, compared with existing baseline models, CorefBERT can achieve significant improvements consistently on various downstream NLP tasks.
  arXiv  Detail & Related papers  (2020-04-15T03:57:45Z)

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.