activity
20192021
most citedPublicly Available Clinical BERT Embeddings

732 citations · 840 across the 9 of their papers we have counts for

collaborators

13 papers

cs.CL2021

Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling

Di Jin, Seokhwan Kim, Dilek Hakkani-Tur

Most prior work on task-oriented dialogue systems are restricted to limited coverage of domain APIs. However, users oftentimes have requests that are out of the scope of these APIs…

cs.CL20213 cited

Adversarial Contrastive Pre-training for Protein Sequences

Matthew B. A. McDermott, Brendan Yap, Harry Hsu +2

Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks. These ideas ha…

cs.IR20213 cited

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang +5

Result relevance prediction is an essential task of e-commerce search engines to boost the utility of search engines and ensure smooth user experience. The last few years eyewitnes…

cs.CL20202 cited

Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis

Xiaoyu Xing, Zhijing Jin, Di Jin +3

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment towards a specific aspect in the text. However, existing ABSA test sets cannot be used to probe whether a model…

cs.LG2020

BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search

Yunjiang Jiang, Yue Shang, Ziyang Liu +6

Relevance has significant impact on user experience and business profit for e-commerce search platform. In this work, we propose a data-driven framework for search relevance predic…

cs.CL202064 cited

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Di Jin, Eileen Pan, Nassim Oufattole +3

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present t…