Papers by Shweta Yadav
Towards Summarizing Healthcare Questions in Low-Resource Setting (2022.coling-1)
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| Challenge: | Existing methods to generate large-scale datasets are difficult in closed domains where human annotation requires domain expertise. |
| Approach: | They propose a method to generate diverse and semantic questions in a low-resource setting with the aim of summarizing healthcare questions. |
| Outcome: | The proposed method generates diverse, fluent, and informative summarized questions on healthcare question summarization datasets. |
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment (N18-2)
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| Challenge: | In recent past, social media has emerged as an active platform in the context of healthcare and medicine. |
| Approach: | They propose to use a novel adversarial learning approach to capture medical sentiments expressed in a medical blog to analyze the user's opinions on health-related issues. |
| Outcome: | The proposed framework can capture the user's opinions on health-related issues at a medical blog level. |
Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction (2021.findings-acl)
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| Challenge: | Existing models based on textual data do not capture context beyond the sentence. |
| Approach: | They propose a framework that enables the model to learn multi-omnics biological information about entities (proteins) with the help of additional multi-modal cues like molecular structure. |
| Outcome: | The proposed model is generalized and optimized for protein-protein interaction task and benefited from additional domain-specific cues. |
Towards Identifying Fine-Grained Depression Symptoms from Memes (2023.acl-long)
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| Challenge: | Mental health disorders are a major economic burden for society and are projected to rise to a staggering US $6 trillion by 2030. |
| Approach: | They propose to use memes to identify fine-grained depression symptoms from memes . they benchmark RESTORE on 20 strong monomodal and multimodal methods . |
| Outcome: | The proposed method can predict fine-grained depression symptoms better than existing models that overlook implicit connections between visual and textual elements of a meme. |
Medical Knowledge-enriched Textual Entailment Framework (2020.coling-main)
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| Challenge: | Existing approaches to achieving robust medical question answering systems lack a textual entailment framework that can capture the con-text beyond the sentence. |
| Approach: | They propose a medical knowledge-enriched textual entailment framework that can acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. |
| Outcome: | The proposed framework achieves 8.27% improvement over existing language models on MEDIQA-RQE dataset. |
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining (P19-1)
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| Challenge: | Existing methods of identifying ADRs are reliable but time-consuming and offer a limited amount of ADR relevant information. |
| Approach: | They propose a neural network-inspired multi-task learning framework that can simultaneously extract ADRs from various sources. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three publicly available real-world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. |
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism (2022.lrec-1)
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| Challenge: | a recent study has established sentiment analysis as an alluring problem, but many feelings are left unexplored. |
| Approach: | They propose a framework to learn the polarity of emotions from Twitter posts . they compare optimism detection with sentiment analysis and hate speech detection . |
| Outcome: | The proposed framework differs between optimistic and pessimistic users on the Optimism/Pessimism Twitter dataset. |
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework (2020.coling-main)
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Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit Sheth, Jeremiah Schumm
| Challenge: | Existing studies on social media for deriving mental health status of users focus on the depression detection task. |
| Approach: | They propose to use a BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. |
| Outcome: | The proposed model improves its robustness and reliability for distinguishing the depression symptoms. |
No perspective, no perception!! Perspective-aware Healthcare Answer Summarization (2024.findings-acl)
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| Challenge: | Healthcare Community Question Answering forums are prone to off-topic discussions and diverse answers can be challenging for readers to sift through. |
| Approach: | They propose a task of perspective-specific answer summarization to identify different perspectives within healthcare-related responses and frame a perspective-driven abstractive summary covering all responses. |
| Outcome: | The proposed model outperforms existing models against five baselines and shows that it is more accurate than existing models. |
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards (2021.acl-short)
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| Challenge: | Existing methods for summarizing long questions are difficult due to the lack of training data and the complexity of the related subtasks. |
| Approach: | They propose a reinforcement learning-based framework for abstractive question summarization that rewards question-type identification and question-focus recognition for regularizing the question generation model. |
| Outcome: | The proposed method achieves higher performance over state-of-the-art models on two benchmark datasets. |
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (2022.coling-1)
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Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, Shweta Yadav
| Challenge: | Health coaching is cost-prohibitive due to its highly personalized nature. |
| Approach: | They propose to build a health coaching dialogue system that converses with patients . they propose to use simplified NLU and NLG frameworks and mechanism-conditioned empathetic response generation. |
| Outcome: | The proposed system generates more empathetic, fluent, and coherent responses . it outperforms the state-of-the-art in NLU tasks while requiring less annotations. |
Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System (L18-1)
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| Challenge: | a study conducted by the pew Internet & American Life Project 1 shows that almost 80 percent of Internet users have explored health-related topic online. |
| Approach: | They propose to crawl medical forums with opinions about medical condition self narrated by users. |
| Outcome: | The proposed system is based on opinions about medical condition self-narrated by users on medical forums. |