Search Results for author: Yeyun Gong

Found 35 papers, 15 papers with code

KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning

no code implementations Findings (EMNLP) 2021 Haonan Li, Yeyun Gong, Jian Jiao, Ruofei Zhang, Timothy Baldwin, Nan Duan

Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation.

Contrastive Learning Natural Language Processing +1

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

CodeRetriever: Unimodal and Bimodal Contrastive Learning

1 code implementation26 Jan 2022 Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan

For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build text-code pairs.

Code Search Contrastive Learning

Adversarial Retriever-Ranker for dense text retrieval

1 code implementation ICLR 2022 Hang Zhang, Yeyun Gong, Yelong Shen, Jiancheng Lv, Nan Duan, Weizhu Chen

To address these challenges, we present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker.

KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning

no code implementations14 Sep 2021 Haonan Li, Yeyun Gong, Jian Jiao, Ruofei Zhang, Timothy Baldwin, Nan Duan

Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation.

Contrastive Learning Natural Language Processing +1

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

Poolingformer: Long Document Modeling with Pooling Attention

no code implementations10 May 2021 Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv, Nan Duan, Weizhu Chen

We first evaluate Poolingformer on two long sequence QA tasks: the monolingual NQ and the multilingual TyDi QA.

Uncertainty-Aware Label Refinement for Sequence Labeling

1 code implementation EMNLP 2020 Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, Xuanjing Huang

Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks.

An Enhanced Knowledge Injection Model for Commonsense Generation

no code implementations COLING 2020 Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang

Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts.

Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing

no code implementations COLING 2020 Bo Shao, Yeyun Gong, Weizhen Qi, Nan Duan, Xiaola Lin

In this paper, we present a multi-level alignment pretraining method in a unified architecture formulti-lingual semantic parsing.

Semantic Parsing

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

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation

2 code implementations3 Apr 2020 Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.

Natural Language Understanding

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

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

Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention

no code implementations COLING 2016 Haoran Huang, Qi Zhang, Yeyun Gong, Xuanjing Huang

By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67. 9{\%} in the F1-score.

Collaborative Filtering General Classification +3

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