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Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Jinhyuk Lee, Zhuyun Dai, Sai Meher Karthik Duddu +4
Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many i…
Multi-Vector Retrieval as Sparse Alignment
Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu +5
Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alig…
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Gyuwan Kim, Jinhyuk Lee, Barlas Oguz +4
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search. However, these proced…
Optimizing Test-Time Query Representations for Dense Retrieval
Mujeen Sung, Jungsoo Park, Jaewoo Kang +2
Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. In this paper, we introduce TOUR (Test-T…
BERN2: an advanced neural biomedical named entity recognition and normalization tool
Mujeen Sung, Minbyul Jeong, Yonghwa Choi +3
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical enti…
Simple Questions Generate Named Entity Recognition Datasets
Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon +2
Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entitie…
Phrase Retrieval Learns Passage Retrieval, Too
Jinhyuk Lee, Alexander Wettig, Danqi Chen
Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval-the most fine-grained retrieval unit-i…
Can Language Models be Biomedical Knowledge Bases?
Mujeen Sung, Jinhyuk Lee, Sean Yi +3
Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs…
Simple Entity-Centric Questions Challenge Dense Retrievers
Christopher Sciavolino, Zexuan Zhong, Jinhyuk Lee +1
Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised trai…
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,…
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…
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…
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…
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…
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi +1
Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to…
Pre-trained Language Model for Biomedical Question Answering
Wonjin Yoon, Jinhyuk Lee, Donghyeon Kim +2
The recent success of question answering systems is largely attributed to pre-trained language models. However, as language models are mostly pre-trained on general domain corpora…
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski +3
Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query. In thi…
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim +4
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting va…
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
Jinhyuk Lee, Seongjun Yun, Hyunjae Kim +2
Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving…
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
Wonjin Yoon, Chan Ho So, Jinhyuk Lee +1
Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical…