Soumya Sharma


2022

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ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts
Rajdeep Mukherjee | Abhinav Bohra | Akash Banerjee | Soumya Sharma | Manjunath Hegde | Afreen Shaikh | Shivani Shrivastava | Koustuv Dasgupta | Niloy Ganguly | Saptarshi Ghosh | Pawan Goyal
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, discussing facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and experts-written short telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarization methods across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple yet effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls.

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Realistic Data Augmentation Framework for Enhancing Tabular Reasoning
Dibyakanti Kumar | Vivek Gupta | Soumya Sharma | Shuo Zhang
Findings of the Association for Computational Linguistics: EMNLP 2022

Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time consuming and thus limits scale, and the latter often produces naive examples that may lack complex reasoning. This paper develops a realistic semi-automated framework for data augmentation for tabular inference. Instead of manually generating a hypothesis for each table, our methodology generates hypothesis templates transferable to similar tables. In addition, our framework entails the creation of rational counterfactual tables based on human written logical constraints and premise paraphrasing. For our case study, we use the INFOTABS (Gupta et al., 2020), which is an entity centric tabular inference dataset. We observed that our framework could generate human-like tabular inference examples, which could benefit training data augmentation, especially in the scenario with limited supervision.

2021

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Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
Abhilash Nandy | Soumya Sharma | Shubham Maddhashiya | Kapil Sachdeva | Pawan Goyal | NIloy Ganguly
Findings of the Association for Computational Linguistics: EMNLP 2021

Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.

2020

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Transfer Learning for Related Languages: Submissions to the WMT20 Similar Language Translation Task
Lovish Madaan | Soumya Sharma | Parag Singla
Proceedings of the Fifth Conference on Machine Translation

In this paper, we describe IIT Delhi’s submissions to the WMT 2020 task on Similar Language Translation for four language directions: Hindi <-> Marathi and Spanish <-> Portuguese. We try out three different model settings for the translation task and select our primary and contrastive submissions on the basis of performance of these three models. For our best submissions, we fine-tune the mBART model on the parallel data provided for the task. The pre-training is done using self-supervised objectives on a large amount of monolingual data for many languages. Overall, our models are ranked in the top four of all systems for the submitted language pairs, with first rank in Spanish -> Portuguese.

2019

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Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs
Soumya Sharma | Bishal Santra | Abhik Jana | Santosh Tokala | Niloy Ganguly | Pawan Goyal
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recently, biomedical version of embeddings obtained from language models such as BioELMo have shown state-of-the-art results for the textual inference task in the medical domain. In this paper, we explore how to incorporate structured domain knowledge, available in the form of a knowledge graph (UMLS), for the Medical NLI task. Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task (ESIM model). We also experiment with fusing the domain-specific sentiment information for the task. Experiments conducted on MedNLI dataset clearly show that this strategy improves the baseline BioELMo architecture for the Medical NLI task.