237 citations · 1.2k across the 87 of their papers we have counts for
13 papers · 1 filter
Accurate Word Representations with Universal Visual Guidance
Zhuosheng Zhang, Haojie Yu, Hai Zhao +2
Word representation is a fundamental component in neural language understanding models. Recently, pre-trained language models (PrLMs) offer a new performant method of contextualize…
Enhancing Pre-trained Language Model with Lexical Simplification
Rongzhou Bao, Jiayi Wang, Zhuosheng Zhang +1
For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given…
Dialogue Graph Modeling for Conversational Machine Reading
Siru Ouyang, Zhuosheng Zhang, Hai Zhao
Conversational Machine Reading (CMR) aims at answering questions in a complicated manner. Machine needs to answer questions through interactions with users based on given rule docu…
Reference Knowledgeable Network for Machine Reading Comprehension
Yilin Zhao, Zhuosheng Zhang, Hai Zhao
Multi-choice Machine Reading Comprehension (MRC) as a challenge requires models to select the most appropriate answer from a set of candidates with a given passage and question. Mo…
Topic-Aware Multi-turn Dialogue Modeling
Yi Xu, Hai Zhao, Zhuosheng Zhang
In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances.…
Multi-span Style Extraction for Generative Reading Comprehension
Junjie Yang, Zhuosheng Zhang, Hai Zhao
Generative machine reading comprehension (MRC) requires a model to generate well-formed answers. For this type of MRC, answer generation method is crucial to the model performance.…
Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue
Longxiang Liu, Zhuosheng Zhang, Hai Zhao +2
A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. Thus utterance- and speaker-aware clues are supposed to be well captured in model…
Composing Answer from Multi-spans for Reading Comprehension
Zhuosheng Zhang, Yiqing Zhang, Hai Zhao +2
This paper presents a novel method to generate answers for non-extraction machine reading comprehension (MRC) tasks whose answers cannot be simply extracted as one span from the gi…
Dialogue-adaptive Language Model Pre-training From Quality Estimation
Junlong Li, Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLMs) have achieved great success on a wide range of natural language processing tasks by virtue of the universal language representation ability obta…
Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
Zhuosheng Zhang, Hai Zhao, Rui Wang
Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burs…
Knowledgeable Dialogue Reading Comprehension on Key Turns
Junlong Li, Zhuosheng Zhang, Hai Zhao
Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-b…
Retrospective Reader for Machine Reading Comprehension
Zhuosheng Zhang, Junjie Yang, Hai Zhao
Machine reading comprehension (MRC) is an AI challenge that requires machine to determine the correct answers to questions based on a given passage. MRC systems must not only answe…
DUMA: Reading Comprehension with Transposition Thinking
Pengfei Zhu, Hai Zhao, Xiaoguang Li
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a…