Search Results for author: Xiaocheng Feng

Found 21 papers, 8 papers with code

A Survey on Dialogue Summarization: Recent Advances and New Frontiers

no code implementations7 Jul 2021 Xiachong Feng, Xiaocheng Feng, Bing Qin

We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.

Text Generation

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

1 code implementation ACL 2021 Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu

Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.

Conversational Response Generation Language Modelling

The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey

no code implementations30 Apr 2021 Yichong Huang, Xiachong Feng, Xiaocheng Feng, Bing Qin

Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text.

Abstractive Text Summarization

Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

1 code implementation7 Dec 2020 Xiachong Feng, Xiaocheng Feng, Bing Qin, Xinwei Geng

First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations.

Data Augmentation Meeting Summarization

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching

no code implementations COLING 2020 Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, Ting Liu

Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.

Few-Shot Learning Language Modelling +2

Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

1 code implementation20 Oct 2020 Xiachong Feng, Xiaocheng Feng, Bing Qin, Ting Liu

In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.

dialogue summary Dialogue Understanding

Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation

1 code implementation24 Feb 2020 Xiaocheng Feng, Yawei Sun, Bing Qin, Heng Gong, Yibo Sun, Wei Bi, Xiaojiang Liu, Ting Liu

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 altering the content.

Style Transfer Text Style Transfer +1

Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning

no code implementations12 Sep 2019 Yibo Sun, Duyu Tang, Nan Duan, Yeyun Gong, Xiaocheng Feng, Bing Qin, Daxin Jiang

Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data.

Meta-Learning Semantic Parsing +1

Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)

1 code implementation IJCNLP 2019 Heng Gong, Xiaocheng Feng, Bing Qin, Ting Liu

To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively.

Table-to-Text Generation Time Series

Adaptive Multi-pass Decoder for Neural Machine Translation

no code implementations EMNLP 2018 Xinwei Geng, Xiaocheng Feng, Bing Qin, Ting Liu

Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored.

Machine Translation Translation

Knowledge Based Machine Reading Comprehension

no code implementations12 Sep 2018 Yibo Sun, Daya Guo, Duyu Tang, Nan Duan, Zhao Yan, Xiaocheng Feng, Bing Qin

Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world.

Machine Reading Comprehension Question Answering +1

Bitext Name Tagging for Cross-lingual Entity Annotation Projection

no code implementations COLING 2016 Dongxu Zhang, Boliang Zhang, Xiaoman Pan, Xiaocheng Feng, Heng Ji, Weiran Xu

Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training.

Named Entity Recognition NER +1

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