A global AI community requires language-diverse publishing
- URL: http://arxiv.org/abs/2408.14772v2
- Date: Thu, 29 Aug 2024 19:50:33 GMT
- Title: A global AI community requires language-diverse publishing
- Authors: Haley Lepp, Parth Sarin,
- Abstract summary: We argue that the requirement for English language publishing upholds and reinforces broader regimes of extraction in AI.
We propose alternative futures for a healthier publishing culture, organized around three themes.
- Score: 1.4579344926652844
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this provocation, we discuss the English dominance of the AI research community, arguing that the requirement for English language publishing upholds and reinforces broader regimes of extraction in AI. While large language models and machine translation have been celebrated as a way to break down barriers, we regard their use as a symptom of linguistic exclusion of scientists and potential readers. We propose alternative futures for a healthier publishing culture, organized around three themes: administering conferences in the languages of the country in which they are held, instructing peer reviewers not to adjudicate the language appropriateness of papers, and offering opportunities to publish and present in multiple languages. We welcome new translations of this piece. Please contact the authors if you would like to contribute one.
Related papers
- Harnessing the Power of Artificial Intelligence to Vitalize Endangered Indigenous Languages: Technologies and Experiences [31.62071644137294]
We discuss the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP.
We report encouraging results in the development of high-quality machine learning translators for Indigenous languages.
We present prototypes we have built in projects done in 2023 and 2024 with Indigenous communities in Brazil, aimed at facilitating writing.
arXiv Detail & Related papers (2024-07-17T14:46:37Z) - Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance [6.907734681124986]
This paper strategically identifies the need for linguistic equity by examining several knowledge editing techniques in multilingual contexts.
We evaluate the performance of models such as Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama across languages including English, German, French, Italian, Spanish, Hindi, Tamil, and Kannada.
arXiv Detail & Related papers (2024-06-17T01:54:27Z) - Wav2Gloss: Generating Interlinear Glossed Text from Speech [78.64412090339044]
We propose Wav2Gloss, a task in which four linguistic annotation components are extracted automatically from speech.
We provide various baselines to lay the groundwork for future research on Interlinear Glossed Text generation from speech.
arXiv Detail & Related papers (2024-03-19T21:45:29Z) - 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) - NusaWrites: Constructing High-Quality Corpora for Underrepresented and
Extremely Low-Resource Languages [54.808217147579036]
We conduct a case study on Indonesian local languages.
We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets.
Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
arXiv Detail & Related papers (2023-09-19T14:42:33Z) - Towards Bridging the Digital Language Divide [4.234367850767171]
multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages.
We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented.
We present a new initiative that aims at reducing linguistic bias through both technological design and methodology.
arXiv Detail & Related papers (2023-07-25T10:53:20Z) - Lost in Translation: Large Language Models in Non-English Content
Analysis [0.0]
Large language models have become the dominant approach for building AI systems to analyze and generate language online.
Recently, researchers and technology companies have attempted to extend the capabilities of large language models into languages other than English.
arXiv Detail & Related papers (2023-06-12T19:10:47Z) - BabySLM: language-acquisition-friendly benchmark of self-supervised
spoken language models [56.93604813379634]
Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels.
We propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels.
We highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
arXiv Detail & Related papers (2023-06-02T12:54:38Z) - Ethical Considerations for Machine Translation of Indigenous Languages:
Giving a Voice to the Speakers [40.84344504873471]
Machine translation has become very successful for high-resource language pairs.
This has sparked new interest in research on the automatic translation of low-resource languages, including Indigenous languages.
arXiv Detail & Related papers (2023-05-31T01:04:20Z) - Crossing the Conversational Chasm: A Primer on Multilingual
Task-Oriented Dialogue Systems [51.328224222640614]
Current state-of-the-art ToD models based on large pretrained neural language models are data hungry.
Data acquisition for ToD use cases is expensive and tedious.
arXiv Detail & Related papers (2021-04-17T15:19:56Z) - Bridging Linguistic Typology and Multilingual Machine Translation with
Multi-View Language Representations [83.27475281544868]
We use singular vector canonical correlation analysis to study what kind of information is induced from each source.
We observe that our representations embed typology and strengthen correlations with language relationships.
We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
arXiv Detail & Related papers (2020-04-30T16:25:39Z)
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.