84 citations · 357 across the 32 of their papers we have counts for
10 papers · 1 filter
Adversarial Training for Code Retrieval with Question-Description Relevance Regularization
Jie Zhao, Huan Sun
Code retrieval is a key task aiming to match natural and programming languages. In this work, we propose adversarial learning for code retrieval, that is regularized by question-de…
Learning a Cost-Effective Annotation Policy for Question Answering
Bernhard Kratzwald, Stefan Feuerriegel, Huan Sun
State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems…
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
Xiang Yue, Xinliang Frederick Zhang, Ziyu Yao +2
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus…
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
Xinliang Frederick Zhang, Heming Sun, Xiang Yue +2
We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set…
Structure-Grounded Pretraining for Text-to-SQL
Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek +3
Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to grou…
TURL: Table Understanding through Representation Learning
Xiang Deng, Huan Sun, Alyssa Lees +2
Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table und…
Rationalizing Medical Relation Prediction from Corpus-level Statistics
Zhen Wang, Jennifer Lee, Simon Lin +1
Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain. Aiming to shed some light on how to rationalize medi…
An Imitation Game for Learning Semantic Parsers from User Interaction
Ziyu Yao, Yiqi Tang, Wen-tau Yih +2
Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy ri…
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
Xiang Yue, Bernal Jimenez Gutierrez, Huan Sun
Machine reading comprehension has made great progress in recent years owing to large-scale annotated datasets. In the clinical domain, however, creating such datasets is quite diff…
Practical Annotation Strategies for Question Answering Datasets
Bernhard Kratzwald, Xiang Yue, Huan Sun +1
Annotating datasets for question answering (QA) tasks is very costly, as it requires intensive manual labor and often domain-specific knowledge. Yet strategies for annotating QA da…