Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations

Kumar, Vinayshekhar Bannihatti; Srinivasan, Ashwin; Chaudhary, Aditi; Route, James; Mitamura, Teruko; Nyberg, Eric

doi:10.18653/v1/w19-5048

Cited by 5 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, several systems used the UMLS (Bodenreider, 2004) to expand acronyms or to replace mentions of medical entities (Bhaskar et al, 2019;Bannihatti Kumar et al, 2019). Data augmentation also played a key role for several systems that used external data to extend batches of in-domain data (Xu et al, 2019), created synthetic data (Bannihatti Kumar et al, 2019), or used models trained on external datasets (e.g. MultiNLI) in ensemble methods (Bhaskar et al, 2019;Sharma and Roychowdhury, 2019).…”

Section: Rqe Approaches and Resultsmentioning

confidence: 99%

“…Precision also ranged from 79.3% to 81.9% for the six first systems. Many teams used their RQE and/or NLI models in the QA task (Bannihatti Kumar et al, 2019;Pugaliya et al, 2019;Zhu et al, 2019;Nguyen et al, 2019). The DUT-NLP team (Zhou et al, 2019b) used an adversarial multi-task network to jointly model RQE and QA.…”

Section: Qa Approaches and Resultsmentioning

confidence: 99%

“…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%

“…The multi-task nature of MEDIQA 2019 was leveraged by teams to train models such as MT-DNN (e.g. (Bannihatti Kumar et al, 2019;Xu et al, 2019;Zhu et al, 2019)). The Sieg team (Bhaskar et al, 2019) trained a model with shared layers being trained for the NLI and RQE tasks.…”

Section: Multi-tasking and External Resourcesmentioning

confidence: 99%

See 3 more Smart Citations

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

View full text Add to dashboard Cite

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

show abstract

Section: Rqe Approaches and Resultsmentioning

confidence: 99%

Section: Qa Approaches and Resultsmentioning

confidence: 99%

Section: Teammentioning

confidence: 99%

Section: Multi-tasking and External Resourcesmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

show abstract

“…The transbronchial biopsy was nondiagnostic. Patient has a mediastinal mass entailment neutral (Wu et al, 2019;Zhu et al, 2019;Xu et al, 2019;Bhaskar et al, 2019;Agrawal et al, 2019;Pugaliya et al, 2019;Bannihatti Kumar et al, 2019;Tawfik and Spruit, 2019;Cengiz et al, 2019), respectively.…”

Section: History Of Heart Attack Entailment Neutralmentioning

confidence: 99%

Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model

Nam

Yoon

Jung

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

View full text Add to dashboard Cite

While deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain. The lack of large datasets and the pervasive use of domain-specific language (i.e. abbreviations and acronyms) in the clinical domain causes slower progress in NLP tasks than that of the general NLP tasks. To fill this gap, we employ word/subword-level based models that adopt large-scale data-driven methods such as pretrained language models and transfer learning in analyzing text for the clinical domain. Empirical results demonstrate the superiority of the proposed methods by achieving 90.6% accuracy in medical domain natural language inference task. Furthermore, we inspect the independent strengths of the proposed approaches in quantitative and qualitative manners. This analysis will help researchers to select necessary components in building models for the medical domain. 1 https://allennlp.org/elmo

show abstract

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

Aftabi,

Seyyedi,

Maleki

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations

Cited by 5 publications

References 9 publications

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

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

Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model

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

Contact Info

Product

Resources

About