Ayan Sengupta


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HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation
Ayan Sengupta | Sourabh Kumar Bhattacharjee | Tanmoy Chakraborty | Md. Shad Akhtar
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Gated Transformer for Robust De-noised Sequence-to-Sequence Modelling
Ayan Sengupta | Amit Kumar | Sourabh Kumar Bhattacharjee | Suman Roy
Findings of the Association for Computational Linguistics: EMNLP 2021

Robust sequence-to-sequence modelling is an essential task in the real world where the inputs are often noisy. Both user-generated and machine generated inputs contain various kinds of noises in the form of spelling mistakes, grammatical errors, character recognition errors, all of which impact downstream tasks and affect interpretability of texts. In this work, we devise a novel sequence-to-sequence architecture for detecting and correcting different real world and artificial noises (adversarial attacks) from English texts. Towards that we propose a modified Transformer-based encoder-decoder architecture that uses a gating mechanism to detect types of corrections required and accordingly corrects texts. Experimental results show that our gated architecture with pre-trained language models perform significantly better that the non-gated counterparts and other state-of-the-art error correction models in correcting spelling and grammatical errors. Extrinsic evaluation of our model on Machine Translation (MT) and Summarization tasks show the competitive performance of the model against other generative sequence-to-sequence models under noisy inputs.


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DATAMAFIA at WNUT-2020 Task 2: A Study of Pre-trained Language Models along with Regularization Techniques for Downstream Tasks
Ayan Sengupta
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

This document describes the system description developed by team datamafia at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. This paper contains a thorough study of pre-trained language models on downstream binary classification task over noisy user generated Twitter data. The solution submitted to final test leaderboard is a fine tuned RoBERTa model which achieves F1 score of 90.8% and 89.4% on the dev and test data respectively. In the later part, we explore several techniques for injecting regularization explicitly into language models to generalize predictions over noisy data. Our experiments show that adding regularizations to RoBERTa pre-trained model can be very robust to data and annotation noises and can improve overall performance by more than 1.2%.