Search Results for author: Jiusheng Chen

Found 9 papers, 7 papers with code

EL-Attention: Memory Efficient Lossless Attention for Generation

1 code implementation11 May 2021 Yu Yan, Jiusheng Chen, Weizhen Qi, Nikhil Bhendawade, Yeyun Gong, Nan Duan, Ruofei Zhang

Transformer model with multi-head attention requires caching intermediate results for efficient inference in generation tasks.

Question Generation

ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training

no code implementations Findings of the Association for Computational Linguistics 2020 Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou

This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.

Abstractive Text Summarization Question Generation

Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space

2 code implementations EMNLP 2020 Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou

In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.

Data Augmentation Machine Reading Comprehension +4

RikiNet: Reading Wikipedia Pages for Natural Question Answering

no code implementations ACL 2020 Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Nan Duan

The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner.

Natural Language Understanding Question Answering

ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training

4 code implementations13 Jan 2020 Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou

This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.

Ranked #5 on Question Generation on SQuAD1.1 (using extra training data)

Abstractive Text Summarization Question Generation

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