Jinhyuk Lee | DeepAI

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∙ 04/04/2023

Rethinking the Role of Token Retrieval in Multi-Vector Retrieval

Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020...

0 Jinhyuk Lee, et al. ∙

∙ 11/02/2022

Multi-Vector Retrieval as Sparse Alignment

Multi-vector retrieval models improve over single-vector dual encoders o...

0 Yujie Qian, et al. ∙

∙ 10/25/2022

Bridging the Training-Inference Gap for Dense Phrase Retrieval

Building dense retrievers requires a series of standard procedures, incl...

0 Gyuwan Kim, et al. ∙

∙ 05/25/2022

Refining Query Representations for Dense Retrieval at Test Time

Dense retrieval uses a contrastive learning framework to learn dense rep...

0 Mujeen Sung, et al. ∙

∙ 01/06/2022

BERN2: an advanced neural biomedical named entity recognition and normalization tool

In biomedical natural language processing, named entity recognition (NER...

0 Mujeen Sung, et al. ∙

∙ 12/16/2021

Simple Questions Generate Named Entity Recognition Datasets

Named entity recognition (NER) is a task of extracting named entities of...

0 Hyunjae Kim, et al. ∙

∙ 09/17/2021

Simple Entity-Centric Questions Challenge Dense Retrievers

Open-domain question answering has exploded in popularity recently due t...

0 Christopher Sciavolino, et al. ∙

∙ 09/16/2021

Phrase Retrieval Learns Passage Retrieval, Too

Dense retrieval methods have shown great promise over sparse retrieval m...

0 Jinhyuk Lee, et al. ∙

∙ 09/15/2021

Can Language Models be Biomedical Knowledge Bases?

Pre-trained language models (LMs) have become ubiquitous in solving vari...

0 Mujeen Sung, et al. ∙

∙ 12/23/2020

Learning Dense Representations of Phrases at Scale

Open-domain question answering can be reformulated as a phrase retrieval...

0 Jinhyuk Lee, et al. ∙

∙ 09/26/2020

Can pandemics transform scientific novelty? Evidence from COVID-19

Scientific novelty is important during the pandemic due to its critical ...

0 Meijun Liu, et al. ∙

∙ 06/29/2020

Answering Questions on COVID-19 in Real-Time

The recent outbreak of the novel coronavirus is wreaking havoc on the wo...

0 Jinhyuk Lee, et al. ∙

∙ 05/01/2020

Biomedical Entity Representations with Synonym Marginalization

Biomedical named entities often play important roles in many biomedical ...

0 Mujeen Sung, et al. ∙

∙ 04/30/2020

Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start a...

0 Miyoung Ko, et al. ∙

∙ 04/29/2020

Adversarial Subword Regularization for Robust Neural Machine Translation

Exposing diverse subword segmentations to neural machine translation (NM...

0 Jungsoo Park, et al. ∙

∙ 11/07/2019

Contextualized Sparse Representation with Rectified N-Gram Attention for Open-Domain Question Answering

A sparse representation is known to be an effective means to encode prec...

0 Jinhyuk Lee, et al. ∙

∙ 09/18/2019

Pre-trained Language Model for Biomedical Question Answering

The recent success of question answering systems is largely attributed t...

0 Wonjin Yoon, et al. ∙

∙ 06/13/2019

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

Existing open-domain question answering (QA) models are not suitable for...

0 Minjoon Seo, et al. ∙

∙ 01/25/2019

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Biomedical text mining is becoming increasingly important as the number ...

0 Jinhyuk Lee, et al. ∙

∙ 01/25/2019

BioBERT: pre-trained biomedical language representation model for biomedical text mining

Biomedical text mining has become more important than ever as the number...

0 Jinhyuk Lee, et al. ∙

∙ 11/09/2018

Typeface Completion with Generative Adversarial Networks

The mood of a text and the intention of the writer can be reflected in t...

10 Yonggyu Park, et al. ∙

∙ 10/01/2018

Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering

Recently, open-domain question answering (QA) has been combined with mac...

0 Jinhyuk Lee, et al. ∙

∙ 09/21/2018

CollaboNet: collaboration of deep neural networks for biomedical named entity recognition

Background: Finding biomedical named entities is one of the most essenti...

0 Wonjin Yoon, et al. ∙

∙ 09/05/2018

Learning User Preferences and Understanding Calendar Contexts for Event Scheduling

With online calendar services gaining popularity worldwide, calendar dat...

0 Donghyeon Kim, et al. ∙

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