Rahul Mishra


2023

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DS4DH at MEDIQA-Chat 2023: Leveraging SVM and GPT-3 Prompt Engineering for Medical Dialogue Classification and Summarization
Boya Zhang | Rahul Mishra | Douglas Teodoro
Proceedings of the 5th Clinical Natural Language Processing Workshop

This paper presents the results of the Data Science for Digital Health (DS4DH) group in the MEDIQA-Chat Tasks at ACL-ClinicalNLP 2023. Our study combines the power of a classical machine learning method, Support Vector Machine, for classifying medical dialogues, along with the implementation of one-shot prompts using GPT-3.5. We employ dialogues and summaries from the same category as prompts to generate summaries for novel dialogues. Our findings exceed the average benchmark score, offering a robust reference for assessing performance in this field.

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Revisiting Automatic Speech Recognition for Tamil and Hindi Connected Number Recognition
Rahul Mishra | Senthil Raja Gunaseela Boopathy | Manikandan Ravikiran | Shreyas Kulkarni | Mayurakshi Mukherjee | Ananth Ganesh | Kingshuk Banerjee
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

Automatic Speech Recognition and its applications are rising in popularity across applications with reasonable inference results. Recent state-of-the-art approaches, often employ significantly large-scale models to show high accuracy for ASR as a whole but often do not consider detailed analysis of performance across low-resource languages applications. In this preliminary work, we propose to revisit ASR in the context of Connected Number Recognition (CNR). More specifically, we (i) present a new dataset HCNR collected to understand various errors of ASR models for CNR, (ii) establish preliminary benchmark and baseline model for CNR, (iii) explore error mitigation strategies and their after-effects on CNR. In the due process, we also compare with end-to-end large scale ASR models for reference, to show its effectiveness.

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Jack-flood at SemEval-2023 Task 5:Hierarchical Encoding and Reciprocal Rank Fusion-Based System for Spoiler Classification and Generation
Sujit Kumar | Aditya Sinha | Soumyadeep Jana | Rahul Mishra | Sanasam Ranbir Singh
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

The rise of social media has exponentially witnessed the use of clickbait posts that grab users’ attention. Although work has been done to detect clickbait posts, this is the first task focused on generating appropriate spoilers for these potential clickbaits. This paper presents our approach in this direction. We use different encoding techniques that capture the context of the post text and the target paragraph. We propose hierarchical encoding with count and document length feature-based model for spoiler type classification which uses Recurrence over Pretrained Encoding. We also propose combining multiple ranking with reciprocal rank fusion for passage spoiler retrieval and question-answering approach for phrase spoiler retrieval. For multipart spoiler retrieval, we combine the above two spoiler retrieval methods. Experimental results over the benchmark suggest that our proposed spoiler retrieval methods are able to retrieve spoilers that are semantically very close to the ground truth spoilers.

2020

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Generating Fact Checking Summaries for Web Claims
Rahul Mishra | Dhruv Gupta | Markus Leippold
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

We present SUMO, a neural attention-based approach that learns to establish correctness of textual claims based on evidence in the form of text documents (e.g., news articles or web documents). SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim. Prior approaches to address the problem of fact checking and evidence extraction have relied on simple concatenation of claim and document word embeddings as an input to claim driven attention weight computation. This is done so as to extract salient words and sentences from the documents that help establish the correctness of the claim. However this design of claim-driven attention fails to capture the contextual information in documents properly. We improve on the prior art by using improved claim and title guided hierarchical attention to model effective contextual cues. We show the efficacy of our approach on political, healthcare, and environmental datasets.