SlowGuess/NAACL_2021_OCR huggingface dataset:
Deepak Gupta, Asif Ekbal, and Pushpak Bhattacharyya. 2020. A semi-supervised approach to generate the code-mixed text using pre-trained encoder and transfer learning. In *Findings of the Association for Computational Linguistics: EMNLP* 2020, Online. Association for Computational Linguistics.
Xinhui Hu, Qi Zhang, Lei Yang, Binbin Gu, and Xinkang Xu. 2020. Data Augmentation for Code-Switch Language Modeling by Fusing Multiple Text Generation Methods. In Proc. Interspeech 2020, pages 1062-1066.
Masoud Jalili Sabet, Philipp Dufter, François Yvon, and Hinrich Schütze. 2020. SimAlign: High quality word alignments without parallel training data using static and contextualized embeddings. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 1627-1643, Online. Association for Computational Linguistics.
Anoop Kunchukuttan. 2020. The IndicNLP Library. https://github.com/anoopkunchukuttan/indic_nlpLibrary/blob/master/docs/indicnlp.pdf.
Anoop Kunchukuttan, Pratik Mehta, and Pushpak Bhattacharyya. 2018. The iit bombay english-hindi parallel corpus. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
Grandee Lee and Haizhou Li. 2020. Modeling code-switch languages using bilingual parallel corpus. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. In Proceedings of NAACL-HLT 2019: Demonstrations.
Adithya Pratapa, Gayatri Bhat, Monjit Choudhury, Sunayana Sitaram, Sandipan Dandapat, and Kalika Bali. 2018. Language modeling for code-mixing: The role of linguistic theory based synthetic data. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1543-1553, Melbourne, Australia. Association for Computational Linguistics.
Bidisha Samanta, Sharmila Reddy, Hussain Jagirdar, Niloy Ganguly, and Soumen Chakrabarti. 2019. A deep generative model for code-switched text. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 5175-5181. AAAI Press.
Vivek Srivastava and Mayank Singh. 2020. PHINC: A parallel Hinglish social media code-mixed corpus for machine translation. In Proceedings of the
Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 41-49, Online. Association for Computational Linguistics.
Karan Taneja, Satarupa Guha, Preethi Jyothi, and Basil Abraham. 2019. Exploiting monolingual speech corpora for code-mixed speech recognition. In INTER-SPEECH, pages 2150-2154.
Jörg Tiedemann. 2012. Parallel data, tools and interfaces in OPUS. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 2214-2218, Istanbul, Turkey. European Language Resources Association (ELRA).
Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, and Pascale Fung. 2019. Code-switched language models using neural based synthetic data from parallel sentences. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL).
Yuan Zhang, Jason Riesa, Dan Gillick, Anton Bakalov, Jason Baldridge, and David Weiss. 2018. A fast, compact, accurate model for language identification of codemixed text. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 328-337.