Simran Khanuja


2021

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MergeDistill: Merging Language Models using Pre-trained Distillation
Simran Khanuja | Melvin Johnson | Partha Talukdar
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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A New Dataset for Natural Language Inference from Code-mixed Conversations
Simran Khanuja | Sandipan Dandapat | Sunayana Sitaram | Monojit Choudhury
Proceedings of the The 4th Workshop on Computational Approaches to Code Switching

Natural Language Inference (NLI) is the task of inferring the logical relationship, typically entailment or contradiction, between a premise and hypothesis. Code-mixing is the use of more than one language in the same conversation or utterance, and is prevalent in multilingual communities all over the world. In this paper, we present the first dataset for code-mixed NLI, in which both the premises and hypotheses are in code-mixed Hindi-English. We use data from Hindi movies (Bollywood) as premises, and crowd-source hypotheses from Hindi-English bilinguals. We conduct a pilot annotation study and describe the final annotation protocol based on observations from the pilot. Currently, the data collected consists of 400 premises in the form of code-mixed conversation snippets and 2240 code-mixed hypotheses. We conduct an extensive analysis to infer the linguistic phenomena commonly observed in the dataset obtained. We evaluate the dataset using a standard mBERT-based pipeline for NLI and report results.

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GLUECoS: An Evaluation Benchmark for Code-Switched NLP
Simran Khanuja | Sandipan Dandapat | Anirudh Srinivasan | Sunayana Sitaram | Monojit Choudhury
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Code-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on cross-lingual and multilingual tasks. We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish. Specifically, our evaluation benchmark includes Language Identification from text, POS tagging, Named Entity Recognition, Sentiment Analysis, Question Answering and a new task for code-switching, Natural Language Inference. We present results on all these tasks using cross-lingual word embedding models and multilingual models. In addition, we fine-tune multilingual models on artificially generated code-switched data. Although multilingual models perform significantly better than cross-lingual models, our results show that in most tasks, across both language pairs, multilingual models fine-tuned on code-switched data perform best, showing that multilingual models can be further optimized for code-switching tasks.

2019

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Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
Pratik Joshi | Christain Barnes | Sebastin Santy | Simran Khanuja | Sanket Shah | Anirudh Srinivasan | Satwik Bhattamishra | Sunayana Sitaram | Monojit Choudhury | Kalika Bali
Proceedings of the 16th International Conference on Natural Language Processing

In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities. While doing so we bring to light the successes and failures of past work in this area, challenges being faced in doing so, and what have they achieved. Throughout this paper, we take a problem-facing approach and describe essential factors which the success of such technologies hinges upon. We present the various aspects in a manner which clarify and lay out the different tasks involved, which can aid organizations looking to make an impact in this area. We take the example of Gondi, an extremely-low resource Indian language, to reinforce and complement our discussion.

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Dependency Parser for Bengali-English Code-Mixed Data enhanced with a Synthetic Treebank
Urmi Ghosh | Dipti Sharma | Simran Khanuja
Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)