mlsanityData to 14 Oct 2026

Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text

arXiv 2108.01861 ↗First posted 4 Aug 2021cs.CLcited by 7

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Abstract (arXiv)

In this shared task, we seek the participating teams to investigate the factors influencing the quality of the code-mixed text generation systems. We synthetically generate code-mixed Hinglish sentences using two distinct approaches and employ human annotators to rate the generation quality. We propose two subtasks, quality rating prediction and annotators' disagreement prediction of the synthetic Hinglish dataset. The proposed subtasks will put forward the reasoning and explanation of the factors influencing the quality and human perception of the code-mixed text.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 4 Aug 2021.

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10 of 24 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  3. 2017The IIT Bombay English-Hindi Parallel Corpuscited by 75
  4. 2020SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweetscited by 62
  5. 2021Are Multilingual Models Effective in Code-Switching?cited by 56
  6. 2020PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translationcited by 38
  7. 2019Overview for the Second Shared Task on Language Identification in Code-Switched Datacited by 33
  8. 2021HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Textcited by 28
  9. 2021Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Textcited by 18
  10. 2021Task-Specific Pre-Training and Cross Lingual Transfer for Code-Switched Datacited by 5

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Citing papers per year: 2022: 5; 2023: 0; 2024: 1; 2025: 1.

7 papers.