mlsanityData to 14 Oct 2026

SentMix-3L: A Bangla-English-Hindi Code-Mixed Dataset for Sentiment Analysis

arXiv 2310.18023 ↗First posted 27 Oct 2023cs.CLcited by 7

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Abstract (arXiv)

Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. Several datasets have been build with the goal of training computational models for code-mixing. Although it is very common to observe code-mixing with multiple languages, most datasets available contain code-mixed between only two languages. In this paper, we introduce SentMix-3L, a novel dataset for sentiment analysis containing code-mixed data between three languages Bangla, English, and Hindi. We carry out a comprehensive evaluation using SentMix-3L. We show that zero-shot prompting with GPT-3.5 outperforms all transformer-based models on SentMix-3L.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 27 Oct 2023.

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9 of 41 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  5. 2021MuRIL: Multilingual Representations for Indian Languagescited by 179
  6. 2021BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Banglacited by 125
  7. 2022L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Modelscited by 34
  8. 2016Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Textcited by 31
  9. 2021Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Fillingcited by 25

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 2.

7 papers.