Challenges and Considerations with Code-Mixed NLP for Multilingual Societies

• URL: http://arxiv.org/abs/2106.07823v1
• Date: Tue, 15 Jun 2021 00:53:55 GMT
• Title: Challenges and Considerations with Code-Mixed NLP for Multilingual Societies
• Authors: Vivek Srivastava, Mayank Singh
• Abstract summary: This paper discusses the current state of the NLP research, limitations, and foreseeable pitfalls in addressing five real-world applications for social good. We also propose futuristic datasets, models, and tools that can significantly advance the current research in multilingual NLP applications for the societal good.
• Score: 1.6675267471157407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingualism refers to the high degree of proficiency in two or more languages in the written and oral communication modes. It often results in language mixing, a.k.a. code-mixing, when a multilingual speaker switches between multiple languages in a single utterance of a text or speech. This paper discusses the current state of the NLP research, limitations, and foreseeable pitfalls in addressing five real-world applications for social good crisis management, healthcare, political campaigning, fake news, and hate speech for multilingual societies. We also propose futuristic datasets, models, and tools that can significantly advance the current research in multilingual NLP applications for the societal good. As a representative example, we consider English-Hindi code-mixing but draw similar inferences for other language pairs

Related papers

• Code-mixed Sentiment and Hate-speech Prediction [2.9140539998069803]
  Large language models have dominated most natural language processing tasks. We created four new bilingual pre-trained masked language models for English-Hindi and English-Slovene languages. We performed an evaluation of monolingual, bilingual, few-lingual, and massively multilingual models on several languages.
  arXiv  Detail & Related papers  (2024-05-21T16:56:36Z)
• Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning [0.7242530499990028]
  Code-switching is the linguistics phenomenon where in casual settings, multilingual speakers mix words from different languages in one utterance. We propose two novel approaches toward improving language identification accuracy on an English-Mandarin child-directed speech dataset. Our best model achieves a balanced accuracy of 0.781 on a real English-Mandarin code-switching child-directed speech corpus and outperforms the previous baseline by 55.3%.
  arXiv  Detail & Related papers  (2023-05-31T11:43:16Z)
• Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages [47.78634360870564]
  We explore prompting multilingual models to generate code-mixed data for seven languages in South East Asia (SEA) We find that publicly available multilingual instruction-tuned models such as BLOOMZ are incapable of producing texts with phrases or clauses from different languages. ChatGPT exhibits inconsistent capabilities in generating code-mixed texts, wherein its performance varies depending on the prompt template and language pairing.
  arXiv  Detail & Related papers  (2023-03-23T18:16:30Z)
• Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
  We generate language representation from multilingual pre-trained models and conduct linguistic analysis. We cluster all the target languages into multiple groups and name each group as a representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
  arXiv  Detail & Related papers  (2021-09-01T09:32:06Z)
• Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
  We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
  arXiv  Detail & Related papers  (2021-03-24T16:20:02Z)
• Revisiting Language Encoding in Learning Multilingual Representations [70.01772581545103]
  We propose a new approach called Cross-lingual Language Projection (XLP) to replace language embedding. XLP projects the word embeddings into language-specific semantic space, and then the projected embeddings will be fed into the Transformer model. Experiments show that XLP can freely and significantly boost the model performance on extensive multilingual benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-16T18:47:10Z)
• Cross-lingual Multispeaker Text-to-Speech under Limited-Data Scenario [10.779568857641928]
  This paper presents an extension on Tacotron2 to achieve bilingual multispeaker speech synthesis. We achieve cross-lingual synthesis, including code-switching cases, between English and Mandarin for monolingual speakers.
  arXiv  Detail & Related papers  (2020-05-21T03:03:34Z)
• That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages [72.9927937955371]
  We use the resources existing in other languages to train a multilingual automatic speech recognition model. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting. Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages.
  arXiv  Detail & Related papers  (2020-05-16T22:28:09Z)
• 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)
• Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity [67.36239720463657]
  Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages. Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs. Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
  arXiv  Detail & Related papers  (2020-03-10T17:17:01Z)

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.