activity
20092023
most citedBioBERT: a pre-trained biomedical language representation model for biomedical text mining

7.5k citations · 8.9k across the 37 of their papers we have counts for

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Showing 2020 · cs.CLShow all

7 papers · 2 filters

cs.CL2020★ 5 cited

Learning Dense Representations of Phrases at Scale

Jinhyuk Lee, Mujeen Sung, Jaewoo Kang +1

Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However,…

cs.CL2020★ 16 cited

Transferability of Natural Language Inference to Biomedical Question Answering

Minbyul Jeong, Mujeen Sung, Gangwoo Kim +4

Biomedical question answering (QA) is a challenging task due to the scarcity of data and the requirement of domain expertise. Pre-trained language models have been used to address…

cs.CL2020★ 7 cited

Answering Questions on COVID-19 in Real-Time

Jinhyuk Lee, Sean S. Yi, Minbyul Jeong +5

The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to…

cs.CL2020★ 8 cited

Biomedical Entity Representations with Synonym Marginalization

Mujeen Sung, Hwisang Jeon, Jinhyuk Lee +1

Biomedical named entities often play important roles in many biomedical text mining tools. However, due to the incompleteness of provided synonyms and numerous variations in their…

cs.CL2020★ 3 cited

Look at the First Sentence: Position Bias in Question Answering

Miyoung Ko, Jinhyuk Lee, Hyunjae Kim +2

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity a…

cs.CL2020★ 2 cited

Adversarial Subword Regularization for Robust Neural Machine Translation

Jungsoo Park, Mujeen Sung, Jinhyuk Lee +1

Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword…

cs.CL2020★ 19 cited

Learning by Semantic Similarity Makes Abstractive Summarization Better

Wonjin Yoon, Yoon Sun Yeo, Minbyul Jeong +2

By harnessing pre-trained language models, summarization models had rapid progress recently. However, the models are mainly assessed by automatic evaluation metrics such as ROUGE.…