1.2k citations · 3k across the 72 of their papers we have counts for
8 papers · 2 filters
Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization
Xiachong Feng, Xiaocheng Feng, Bing Qin +1
Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as…
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
Xiachong Feng, Xiaocheng Feng, Bing Qin +1
Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker…
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
Tianwen Jiang, Tong Zhao, Bing Qin +3
Noun phrases and relational phrases in Open Knowledge Bases are often not canonical, leading to redundant and ambiguous facts. In this work, we integrate structural information (fr…
How Does Selective Mechanism Improve Self-Attention Networks?
Xinwei Geng, Longyue Wang, Xing Wang +3
Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words. However, the underly…
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Yiming Cui, Wanxiang Che, Ting Liu +3
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further imp…
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Jiaqi Li, Ming Liu, Min-Yen Kan +5
Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse s…
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
Xiaocheng Feng, Yawei Sun, Bing Qin +5
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while alter…
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Zhangyin Feng, Daya Guo, Duyu Tang +8
We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream N…