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README.md

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 pretty_name: Hindi-English Codemix Datasets
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 - 1M<n<10M
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+---
+# Dataset Card for HINMIX hi-en
+
+<!-- Provide a quick summary of the dataset. -->
+**HINMIX is a massive parallel codemixed dataset for Hindi-English code switching.**
+
+See the [📚 paper on arxiv](https://arxiv.org/abs/2403.16771) to dive deep into this synthetic codemix data generation pipeline. 
+Dataset contains 4.2M parallel sentences in 6 Hindi-English forms.
+
+Further, we release gold standard codemix dev and test set manually translated by proficient bilingual annotators.
+- Dev Set consists of 280 examples
+- Test set consists of 2507 examples
+
+## Dataset Details
+
+### Dataset Description
+
+We construct a synthetic Hinglish-English dataset by leveraging a bilingual Hindi-English corpus.
+
+- **Curated by:** LCS2 IIITD (https://www.lcs2.in/)
+- **Language(s) (NLP):** Hindi Romanized, Hindi Devanagiri, Hindi Codemix, English
+
+### Dataset Sources [optional]
+
+- **Repository:** https://github.com/Kartikaggarwal98/Robust_Codemix_MT
+- **Paper:** https://arxiv.org/abs/2403.16771
+
+## Uses
+
+Dataset can be used individually to train machine translation models for codemix hindi translation in any direction.
+Dataset can be appended with other languages from similar language family to transfer codemixing capabilities in a zero shot manner. 
+Zero-shot translation on bangla-english showed great performance without even developing bangla codemix corpus. 
+An indic-multilingual model with this data as a subset can improve codemixing by a significant margin.
+
+### Source Data
+
+[IITB Parallel corpus](https://www.cfilt.iitb.ac.in/iitb_parallel/) is chosen as the base dataset to translate into codemix forms. 
+The corpus contains widely diverse content from news articles, judicial domain, indian government websites, wikipedia, book translations, etc.
+
+#### Data Collection and Processing
+
+1. Given a source- target sentence pair S || T , we generate the synthetic code-mixed data by substituting words in the matrix language sentence with the corresponding words from the embedded language sentence. 
+Here, hindi is the matrix language which forms the syntactic and morphological structure of CM sentence. English becomes the embedded language from which we borrow words.
+1. Create inclusion list of nouns, adjectives and quantifiers which are candidates for substitution.
+1. POS-tag the corpus using any tagger. We used [LTRC](http://ltrc.iiit.ac.in/analyzer/) for hindi tagging.
+1. Use fast-align for learning alignment model b/w parallel corpora (Hi-En). Once words are aligned, next task is switch words from english sentences to hindi sentence based on inclusion list.
+1. Use heuristics to replace n-gram words and create multiple codemix mappings of the same hindi sentence.
+1. Filter sentences using deterministic and perplexity metrics from a multilingual model like XLM.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61565c721b6f2789680793eb/KhhuM9Ze2-UrllHh6vRGL.png)
+
+
+### Recommendations
+
+Sringent filtering by deduplication of similar sentences and removing the ungrammatical sentences can be useful for training high quality models.
+
+## Citation Information
+
+@misc{kartik2024synthetic,
+      title={Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation}, 
+      author={Kartik and Sanjana Soni and Anoop Kunchukuttan and Tanmoy Chakraborty and Md Shad Akhtar},
+      year={2024},
+      eprint={2403.16771},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+
+## Dataset Card Contact
+
+kartik@ucsc.edu