{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/calbert-code-mixed-adaptive-language-1","title":"CalBERT - Code-mixed Adaptive Language representations using BERT","arxiv_id":null,"date":"2022-04-14","proceeding":"AAAI-MAKE 2022 4","authors":["Aditeya Baral","Ansh Sarkar","Aronya Baksy","Deeksha D","Ashwini M Joshi"],"abstract":"A code-mixed language is a type of language that involves the combination of two or more language varieties in its script or speech. Analysis of code-text is difficult to tackle because the language present is not consistent and does not work with existing monolingual approaches. We propose a novel approach to improve performance in Transformers by introducing an additional step called \"Siamese Pre-Training\", which allows pre-trained monolingual Transformers to adapt language representations for code-mixed languages with a few examples of code-mixed data. The proposed architectures beat the state of the art F1-score on the Sentiment Analysis for Indian Languages (SAIL) dataset, with the highest possible improvement being 5.1 points, while also achieving the state-of-the-art accuracy on the IndicGLUE Product Reviews dataset by beating the benchmark by 0.4 points.","url_abs":"https://ceur-ws.org/Vol-3121/short3.pdf","url_pdf":"https://ceur-ws.org/Vol-3121/short3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"calbert-code-mixed-adaptive-language-1","repo_url":"https://github.com/aditeyabaral/calbert","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-iitp-product-reviews","task":"Sentiment Analysis","dataset":"IITP Product Reviews Sentiment","model":"CalBERT","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"79.4"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-sail-2017","task":"Sentiment Analysis","dataset":"SAIL 2017","model":"CalBERT","rank_in_archive_order":1,"of":1,"metrics":{"F1":"62","Precision":"61.8","Recall":"61.8"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}