Search Results for author: Jingfang Xu

Found 14 papers, 11 papers with code

Transfer Learning for Sequence Generation: from Single-source to Multi-source

1 code implementation ACL 2021 Xuancheng Huang, Jingfang Xu, Maosong Sun, Yang Liu

Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient.

Automatic Post-Editing Document Summarization +3

ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks

1 code implementation16 Jan 2021 Bingning Wang, Ting Yao, WeiPeng Chen, Jingfang Xu, Xiaochuan Wang

In compositional question answering, the systems should assemble several supporting evidence from the document to generate the final answer, which is more difficult than sentence-level or phrase-level QA.

Answer Selection Machine Reading Comprehension

Modeling Voting for System Combination in Machine Translation

1 code implementation14 Jul 2020 Xuancheng Huang, Jiacheng Zhang, Zhixing Tan, Derek F. Wong, Huanbo Luan, Jingfang Xu, Maosong Sun, Yang Liu

System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance.

Machine Translation Translation

ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion

1 code implementation22 Jun 2020 BingningWang, Ting Yao, Qi Zhang, Jingfang Xu, Xiaochuan Wang

The release of ReCO consists of 300k questions that to our knowledge is the largest in Chinese reading comprehension.

Causal Inference Chinese Reading Comprehension +2

A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction

1 code implementation ACL 2020 Yilin Niu, Fangkai Jiao, Mantong Zhou, Ting Yao, Jingfang Xu, Minlie Huang

Neural models have achieved great success on machine reading comprehension (MRC), many of which typically consist of two components: an evidence extractor and an answer predictor.

Machine Reading Comprehension Multi-Choice MRC +1

Neural Machine Translation with Explicit Phrase Alignment

no code implementations26 Nov 2019 Jiacheng Zhang, Huanbo Luan, Maosong Sun, FeiFei Zhai, Jingfang Xu, Yang Liu

The lack of alignment in NMT models leads to three problems: it is hard to (1) interpret the translation process, (2) impose lexical constraints, and (3) impose structural constraints.

Machine Translation Translation

Learning to Copy for Automatic Post-Editing

2 code implementations IJCNLP 2019 Xuancheng Huang, Yang Liu, Huanbo Luan, Jingfang Xu, Maosong Sun

To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.

Automatic Post-Editing Translation

Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization

1 code implementation ACL 2017 Jiacheng Zhang, Yang Liu, Huanbo Luan, Jingfang Xu, Maosong Sun

Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge.

Machine Translation Translation

Improving the Transformer Translation Model with Document-Level Context

3 code implementations EMNLP 2018 Jiacheng Zhang, Huanbo Luan, Maosong Sun, FeiFei Zhai, Jingfang Xu, Min Zhang, Yang Liu

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.

Translation

Three Strategies to Improve One-to-Many Multilingual Translation

no code implementations EMNLP 2018 Yining Wang, Jiajun Zhang, FeiFei Zhai, Jingfang Xu, Cheng-qing Zong

However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models.

Machine Translation Multi-Task Learning +1

Assigning personality/identity to a chatting machine for coherent conversation generation

1 code implementation9 Jun 2017 Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu

Endowing a chatbot with personality or an identity is quite challenging but critical to deliver more realistic and natural conversations.

Chatbot

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