6 citations · 15 across the 5 of their papers we have counts for
10 papers · 1 filter
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
Dian Yu, Kai Sun, Dong Yu +1
In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks. In this pape…
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge
Kai Sun, Dian Yu, Jianshu Chen +2
In this paper, we aim to extract commonsense knowledge to improve machine reading comprehension. We propose to represent relations implicitly by situating structured knowledge in a…
Dialogue-Based Relation Extraction
Dian Yu, Kai Sun, Claire Cardie +1
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in…
CLUE: A Chinese Language Understanding Evaluation Benchmark
Liang Xu, Hai Hu, Xuanwei Zhang +29
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These com…
Improving Pre-Trained Multilingual Models with Vocabulary Expansion
Hai Wang, Dian Yu, Kai Sun +2
Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely reso…
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Kai Sun, Dian Yu, Dong Yu +1
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chine…