most citedGraph Transformer Networks: Learning Meta-path Graphs to Improve GNNs

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2022

Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework

Wonjin Yoon, Richard Jackson, Elliot Ford +2

In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora. Decades of s…

cs.CL2022

Lack of Fluency is Hurting Your Translation Model

Jaehyo Yoo, Jaewoo Kang

Many machine translation models are trained on bilingual corpus, which consist of aligned sentence pairs from two different languages with same semantic. However, there is a qualit…

cs.CL20211 cited

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…

cs.CL2021

Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering

Gangwoo Kim, Hyunjae Kim, Jungsoo Park +1

One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do no…

cs.LG20212 cited

Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs

Seongjun Yun, Minbyul Jeong, Sungdong Yoo +5

Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing G…