Search Results for author: Yu Yan

Found 15 papers, 10 papers with code

A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation

no code implementations23 May 2022 Weizhen Qi, Yeyun Gong, Yelong Shen, Jian Jiao, Yu Yan, Houqiang Li, Ruofei Zhang, Weizhu Chen, Nan Duan

To further illustrate the commercial value of our approach, we conduct experiments on three generation tasks in real-world advertisements applications.

Question Generation Text Generation

Factorisation-based Image Labelling

1 code implementation19 Nov 2021 Yu Yan, Yael Balbastre, Mikael Brudfors, John Ashburner

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging.

Brain Segmentation

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

ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine

no code implementations21 Oct 2020 Weizhen Qi, Yeyun Gong, Yu Yan, Jian Jiao, Bo Shao, Ruofei Zhang, Houqiang Li, Nan Duan, Ming Zhou

We build a dataset from a real-word sponsored search engine and carry out experiments to analyze different generative retrieval models.

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

Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation

1 code implementation EMNLP 2020 Dayiheng Liu, Yeyun Gong, Jie Fu, Wei Liu, Yu Yan, Bo Shao, Daxin Jiang, Jiancheng Lv, Nan Duan

Furthermore, we propose a simple and effective method to mine the keyphrases of interest in the news article and build a first large-scale keyphrase-aware news headline corpus, which contains over 180K aligned triples of $<$news article, headline, keyphrase$>$.

Headline generation

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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