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The Multilingual Nature of Scientific Literature and the Need for Diverse Models


Core Concepts
Scientific literature is predominantly multilingual, necessitating diverse models for accurate representation.
Abstract
English-centric focus shifting in NLP research. Multilingual support in pretrained models. Importance of language agnostic methods. Lack of multilingual support in scientific document representation. Real-world impacts of English-only models. Future directions for multilingual support.
Stats
English makes up 85.11% of the literature. Chinese papers have 92.95% and 93.14% unknown tokens in models. Multilingual models have 0.06% unknown tokens.
Quotes
"English has long been assumed the lingua franca of scientific research." "English-only models fail to create meaningful representations for many languages."

Deeper Inquiries

How can the NLP community improve support for non-English documents?

To improve support for non-English documents, the NLP community can take several steps. Firstly, there should be a shift towards training multilingual models that can effectively process and represent text in various languages. These models should have robust language agnostic capabilities to ensure accurate representation of non-English texts. Additionally, creating multilingual benchmark datasets specifically designed for scientific document representation can help evaluate the performance of models on different languages. This will motivate researchers to train models on a wider range of languages and improve the inclusivity of language technologies. Furthermore, incorporating language detection and translation mechanisms into the models can help identify the language of the input text and provide appropriate outputs or warnings for unsupported languages. By adopting a combination of text-based and graph-based approaches, the NLP community can enhance the support for non-English documents and promote linguistic diversity in scientific research.

What are the implications of using English-only models beyond the scientific domain?

The implications of using English-only models extend beyond the scientific domain and have broader consequences. One significant impact is the perpetuation of linguistic bias and exclusion of non-English speaking communities from accessing and contributing to knowledge and information. By relying solely on English models, there is a risk of misrepresentation, misinterpretation, and loss of valuable insights present in non-English texts. This can hinder cross-cultural collaboration, limit the dissemination of research findings, and reinforce the dominance of English as the primary language of communication in academic and professional settings. Moreover, the use of English-only models can lead to inaccurate translations, miscommunications, and cultural insensitivity, affecting user experience and hindering effective communication across diverse linguistic backgrounds. Overall, the reliance on English-only models can perpetuate language inequalities, hinder global knowledge sharing, and limit the inclusivity of language technologies in various domains.

How can the lack of multilingual evaluation datasets be addressed effectively?

The lack of multilingual evaluation datasets can be effectively addressed by creating comprehensive benchmark datasets that cover a wide range of languages and document types. These datasets should include samples from various linguistic backgrounds, ensuring representation from low-resource and endangered languages as well. By developing multilingual benchmarks specifically tailored for scientific document representation, researchers can evaluate the performance of models on different languages and assess their multilingual capabilities accurately. Additionally, the creation of benchmark datasets can serve as a motivating factor for researchers to train multilingual models and enhance their support for non-English documents. Collaborative efforts within the NLP community to curate and release multilingual evaluation datasets will facilitate the development of more inclusive language technologies and promote linguistic diversity in research and communication.
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