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PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality

arXiv 2206.07988 ↗First posted 16 Jun 2022cs.AIcited by 0

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Abstract (arXiv)

Code-Mixing is a phenomenon of mixing two or more languages in a speech event and is prevalent in multilingual societies. Given the low-resource nature of Code-Mixing, machine generation of code-mixed text is a prevalent approach for data augmentation. However, evaluating the quality of such machine generated code-mixed text is an open problem. In our submission to HinglishEval, a shared-task collocated with INLG2022, we attempt to build models factors that impact the quality of synthetically generated code-mix text by predicting ratings for code-mix quality.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 16 Jun 2022.

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References · 7

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  2. 2019How multilingual is Multilingual BERT?cited by 645
  3. 2020Language-agnostic BERT Sentence Embeddingcited by 627
  4. 2020GLUECoS : An Evaluation Benchmark for Code-Switched NLPcited by 102
  5. 2021HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Textcited by 28
  6. 2016Challenges of Computational Processing of Code-Switchingcited by 20
  7. 2021Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Textcited by 7

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