GlobalBench: A Benchmark for Global Progress in Natural Language Processing

• URL: http://arxiv.org/abs/2305.14716v1
• Date: Wed, 24 May 2023 04:36:32 GMT
• Title: GlobalBench: A Benchmark for Global Progress in Natural Language Processing
• Authors: Yueqi Song, Catherine Cui, Simran Khanuja, Pengfei Liu, Fahim Faisal, Alissa Ostapenko, Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Yulia Tsvetkov, Antonios Anastasopoulos and Graham Neubig
• Abstract summary: GlobalBench aims to track progress on all NLP datasets in all languages. Tracks estimated per-speaker utility and equity of technology across all languages. Currently, GlobalBench covers 966 datasets in 190 languages, and has 1,128 system submissions spanning 62 languages.
• Score: 114.24519009839142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-optimal incentives to work on less resourced languages. To track and further incentivize the global development of equitable language technology, we introduce GlobalBench. Prior multilingual benchmarks are static and have focused on a limited number of tasks and languages. In contrast, GlobalBench is an ever-expanding collection that aims to dynamically track progress on all NLP datasets in all languages. Rather than solely measuring accuracy, GlobalBench also tracks the estimated per-speaker utility and equity of technology across all languages, providing a multi-faceted view of how language technology is serving people of the world. Furthermore, GlobalBench is designed to identify the most under-served languages, and rewards research efforts directed towards those languages. At present, the most under-served languages are the ones with a relatively high population, but nonetheless overlooked by composite multilingual benchmarks (like Punjabi, Portuguese, and Wu Chinese). Currently, GlobalBench covers 966 datasets in 190 languages, and has 1,128 system submissions spanning 62 languages.

Related papers

• BayLing 2: A Multilingual Large Language Model with Efficient Language Alignment [42.193395498828764]
  We introduce BayLing 2, which efficiently transfers generative capabilities and knowledge from high-resource languages to low-resource languages. For multilingual translation across 100+ languages, BayLing shows superior performance compared to open-source models of similar scale. Demo, homepage, code and models of BayLing are available.
  arXiv  Detail & Related papers  (2024-11-25T11:35:08Z)
• IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages [12.514648269553104]
  IndicGenBench is the largest benchmark for evaluating large language models (LLMs) It is composed of diverse generation tasks like cross-lingual summarization, machine translation, and cross-lingual question answering. The largest PaLM-2 models performs the best on most tasks, however, there is a significant performance gap in all languages compared to English.
  arXiv  Detail & Related papers  (2024-04-25T17:57:36Z)
• 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)
• Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages [54.832599498774464]
  We propose to build multilingual word embeddings (MWEs) via a novel language chain-based approach. We build MWEs one language at a time by starting from the resource rich source and sequentially adding each language in the chain till we reach the target. We evaluate our method on bilingual lexicon induction for 4 language families, involving 4 very low-resource (5M tokens) and 4 moderately low-resource (50M) target languages.
  arXiv  Detail & Related papers  (2023-11-21T09:59:29Z)
• Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages [62.730361829175415]
  MIRACL is a multilingual dataset we have built for the WSDM 2023 Cup challenge. It focuses on ad hoc retrieval across 18 different languages. Our goal is to spur research that will improve retrieval across a continuum of languages.
  arXiv  Detail & Related papers  (2022-10-18T16:47:18Z)
• NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages [100.59889279607432]
  We focus on developing resources for languages in Indonesia. Most languages in Indonesia are categorized as endangered and some are even extinct. We develop the first-ever parallel resource for 10 low-resource languages in Indonesia.
  arXiv  Detail & Related papers  (2022-05-31T17:03:50Z)
• Meta-X$_{NLG}$: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation [11.155430893354769]
  This paper proposes a novel meta-learning framework to learn shareable structures from typologically diverse languages. We first cluster the languages based on language representations and identify the centroid language of each cluster. A meta-learning algorithm is trained with all centroid languages and evaluated on the other languages in the zero-shot setting.
  arXiv  Detail & Related papers  (2022-03-19T05:22:07Z)
• SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection [81.85463892070085]
  The SIGMORPHON 2020 task on morphological reinflection aims to investigate systems' ability to generalize across typologically distinct languages. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages.
  arXiv  Detail & Related papers  (2020-06-20T13:24:14Z)
• 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.