IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering

Bandyopadhyay, Dibyanayan; Gain, Baban; Saikh, Tanik; Ekbal, Asif

doi:10.18653/v1/w19-5056

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…As Table VII indicates, even pretrained transformers on biological data, i.e., BioBERT, are beaten by the ReQuEST. However, the 1% supremacy of [61] suggests that the quality of text representation directly influences classifier performance.…”

Section: G Analysis Of Request Performance On Other Datasetsmentioning

confidence: 99%

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Aftabi,

Seyyedi,

Maleki

et al. 2024

IEEE Access

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The burgeoning popularity of community question-answering platforms as an informationseeking strategy has prompted researchers to look for ways to save response time and effort, among which question entailment recognizing, question summarizing, and question tagging are prominent. However, none has investigated the implicit relations between these tasks and the benefits their interaction could provide. In this study, ReQuEST, a novel multi-task model based on bidirectional auto-regressive transformers (BART), is introduced to recognize question entailment, summarize questions respecting given queries, and tag questions with primary topics, simultaneously. ReQuEST comprises one shared encoder representing input sequences, two half-shared decoders providing intermediate presentations, and three task-specific heads producing summaries, tags, and entailed questions. A lightweight fine-tuning technique and a weighted loss function help us learn model parameters efficiently. With roughly 187k learning parameters, ReQuEST is almost half the size of BARTlarge and is two-thirds smaller than its multi-task counterparts. Empirical experiments on standard summarization datasets reveal that ReQuEST outperforms competitors on Debatepedia with a Rouge-L of 46.77 and has persuasive performance with a Rouge-L of 37.37 on MeQSum. On MediQA-RQE as a medical benchmark for entailment recognition, ReQuEST is also comparable in accuracy with state-of-the-art systems without being pre-trained on domain-specific datasets.

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Section: G Analysis Of Request Performance On Other Datasetsmentioning

confidence: 99%

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Aftabi,

Seyyedi,

Maleki

et al. 2024

IEEE Access

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“…Liu et al [25] trained heterogeneous base models by the bagging method and then integrated them. Bandyopadhyay et al [26] integrated BioBERT models pre-trained on the different corpus to achieve better results. Yang et al [27] presented an adaptive decision fusion method, which adaptively combines classifiers with different levels of features to cultivate an answer selection model with robustness and effectiveness.…”

Section: Question Answering Based On Model Integrationmentioning

confidence: 99%

Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine

Zhai

Fang

Cui

2022

Mobile Information Systems

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With the development of the Internet of Things, intelligent medical devices and intelligent consultation platforms have been rapidly popularized, providing great convenience for medical treatment to patients and consultation to doctors. In the face of large-scale medical electronic information data, how to automatically and accurately learn professional knowledge and realize application is very important. The existing intelligent medical question answering models typically use query expansion to improve the accuracy of model matching answers but ignore the corresponding entity association between questions and answers, and the method of randomly generating negative samples cannot train the model to capture more semantic information. To solve these problems, a question answering method based on dual-dimensional entity association for intelligent medicine is proposed. This method learns semantics from the dual-dimension of question and answer respectively. In the question dimension, query extension words with strong relevance to query intention are obtained through entity association in the medical knowledge graph. In the answer dimension, answer sentences are segmented and sampled by employing a variety of similarity distances to generate negative samples in different ranges, provide different levels of correlation information between entities for model training, and then integrate the trained model to improve the accuracy and robustness of the question answering model. The experimental results show that the question answering model proposed in this paper has a good improvement in accuracy.

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“…Task(s) ANU-CSIRO (Nguyen et al, 2019) NLI, RQE, QA ARS NITK (Agrawal et al, 2019) NLI, RQE, QA DoubleTransfer (Xu et al, 2019) NLI, RQE, QA Dr.Quad (Bannihatti Kumar et al, 2019) NLI, RQE, QA DUT-BIM (Zhou et al, 2019a) QA DUT-NLP (Zhou et al, 2019b) RQE, QA IITP (Bandyopadhyay et al, 2019) NLI, RQE, QA IIT-KGP (Sharma and Roychowdhury, 2019) RQE KU ai (Cengiz et al, 2019) NLI lasigeBioTM (Lamurias and Couto, 2019) NLI, RQE, QA MSIT SRIB (Chopra et al, 2019) NLI NCUEE (Lee et al, 2019b) NLI PANLP (Zhu et al, 2019) NLI, RQE, QA Pentagon (Pugaliya et al, 2019) NLI, RQE, QA Saama Research (Kanakarajan, 2019) NLI Sieg (Bhaskar et al, 2019) NLI, RQE Surf (Nam et al, 2019) NLI UU TAILS (Tawfik and Spruit, 2019) NLI, RQE UW-BHI (Kearns et al, 2019) NLI WTMED (Wu et al, 2019) NLI which builds up on BERT to perform multi-task learning and is evaluated on the GLUE benchmark (Wang et al, 2018). A common theme across all the papers was training of multiple models and then using an ensemble as the final system which performed better than the individual models.…”

Section: Teammentioning

confidence: 99%

“…Below is a nonexhaustive list resources used by various teams. • Word Embeddings While many teams used BERT (Lamurias and Couto, 2019;Zhou et al, 2019a;Bandyopadhyay et al, 2019;Nguyen et al, 2019;Sharma and Roychowdhury, 2019) 14 , some teams also used word embeddings as the input to their models. Bhaskar et al ( 2019) used biomedical word embeddings from Chen et al (2018) while Kearns et al (2019) used cui2vec (Beam et al, 2018.…”

Section: Multi-tasking and External Resourcesmentioning

confidence: 99%

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

Abacha¹,

Shivade²,

Demner‐Fushman³

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

118

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This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems. MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain. 72 teams participated in the challenge, achieving an accuracy of 98% in the NLI task, 74.9% in the RQE task, and 78.3% in the QA task. In this paper, we describe the tasks, the datasets, and the participants' approaches and results. We hope that this shared task will attract further research efforts in textual inference, question entailment, and question answering in the medical domain. 12 metamap.nlm.nih.gov 13 github.com/abachaa/MedQuAD 14 github.com/Team-IIT-KGP/Qspider

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IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering

Cited by 5 publications

References 9 publications

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

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