no code implementations • EACL (AdaptNLP) 2021 • Sebastin Santy, Anirudh Srinivasan, Monojit Choudhury
Models such as mBERT and XLMR have shown success in solving Code-Mixed NLP tasks even though they were not exposed to such text during pretraining.
1 code implementation • 29 Oct 2023 • Anirudh Srinivasan, Venkata S Govindarajan, Kyle Mahowald
We use one such technique, AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models (mBERT and XLM-R).
1 code implementation • 24 May 2023 • Anuj Diwan, Anirudh Srinivasan, David Harwath, Eunsol Choi
We train and evaluate our models for English-to-German, German-to-English and Marathi-to-English translation on three different domains (European Parliament, Common Voice, and All India Radio) with single-speaker synthesized speech data.
1 code implementation • 29 Nov 2022 • Anirudh Srinivasan, Eunsol Choi
We study politeness phenomena in nine typologically diverse languages.
no code implementations • 19 Feb 2022 • Shuguang Chen, Gustavo Aguilar, Anirudh Srinivasan, Mona Diab, Thamar Solorio
For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions.
no code implementations • 17 Oct 2021 • Anirudh Srinivasan, Sunayana Sitaram, Tanuja Ganu, Sandipan Dandapat, Kalika Bali, Monojit Choudhury
Recent advancements in NLP have given us models like mBERT and XLMR that can serve over 100 languages.
1 code implementation • EACL 2021 • Mohd Sanad Zaki Rizvi, Anirudh Srinivasan, Tanuja Ganu, Monojit Choudhury, Sunayana Sitaram
Code-mixing is common in multilingual communities around the world, and processing it is challenging due to the lack of labeled and unlabeled data.
no code implementations • SEMEVAL 2020 • Anirudh Srinivasan
In this paper, we present our system for the SemEval 2020 task on code-mixed sentiment analysis.
no code implementations • ACL 2020 • Simran Khanuja, D, S apat, ipan, Anirudh Srinivasan, Sunayana Sitaram, Monojit Choudhury
We present results on all these tasks using cross-lingual word embedding models and multilingual models.
no code implementations • LREC 2020 • Anirudh Srinivasan, D, S apat, ipan, Monojit Choudhury
In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees.
no code implementations • 26 Apr 2020 • Simran Khanuja, Sandipan Dandapat, Anirudh Srinivasan, Sunayana Sitaram, Monojit Choudhury
We present results on all these tasks using cross-lingual word embedding models and multilingual models.
no code implementations • ICON 2019 • Pratik Joshi, Christain Barnes, Sebastin Santy, Simran Khanuja, Sanket Shah, Anirudh Srinivasan, Satwik Bhattamishra, Sunayana Sitaram, Monojit Choudhury, Kalika Bali
In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities.
1 code implementation • 1 Dec 2019 • Anirudh Srinivasan, Dzmitry Bahdanau, Maxime Chevalier-Boisvert, Yoshua Bengio
In this paper, we present a technique that improves the process of training an agent (using RL) for instruction following.