Search Results for author: Dayiheng Liu

Found 39 papers, 18 papers with code

GCPG: A General Framework for Controllable Paraphrase Generation

no code implementations Findings (ACL) 2022 Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen

Under GCPG, we reconstruct commonly adopted lexical condition (i. e., Keywords) and syntactical conditions (i. e., Part-Of-Speech sequence, Constituent Tree, Masked Template and Sentential Exemplar) and study the combination of the two types.

Paraphrase Generation

RoBLEURT Submission for WMT2021 Metrics Task

no code implementations WMT (EMNLP) 2021 Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao

After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.

Denoising

Draft, Command, and Edit: Controllable Text Editing in E-Commerce

no code implementations11 Aug 2022 Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Qian Qu, Jiancheng Lv

To address this challenge, we explore a new draft-command-edit manner in description generation, leading to the proposed new task-controllable text editing in E-commerce.

Data Augmentation

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

1 code implementation NAACL 2022 Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi

In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.

Relation Extraction

Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation

no code implementations28 Apr 2022 Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, Jun Xie

We experimentally find that these prompts can be simply concatenated as a whole to multi-attribute CTG without any re-training, yet raises problems of fluency decrease and position sensitivity.

Text Generation

RoBLEURT Submission for the WMT2021 Metrics Task

no code implementations28 Apr 2022 Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao

After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.

Denoising

RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation

no code implementations1 Mar 2022 Yidan Zhang, Yu Wan, Dayiheng Liu, Baosong Yang, Zhenan He

Recently, Minimum Bayes Risk (MBR) decoding has been proposed to improve the quality for NMT, which seeks for a consensus translation that is closest on average to other candidates from the n-best list.

Machine Translation Translation

Frequency-Aware Contrastive Learning for Neural Machine Translation

no code implementations29 Dec 2021 Tong Zhang, Wei Ye, Baosong Yang, Long Zhang, Xingzhang Ren, Dayiheng Liu, Jinan Sun, Shikun Zhang, Haibo Zhang, Wen Zhao

Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective.

Contrastive Learning Machine Translation +2

KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation

no code implementations15 Dec 2021 Xin Liu, Dayiheng Liu, Baosong Yang, Haibo Zhang, Junwei Ding, Wenqing Yao, Weihua Luo, Haiying Zhang, Jinsong Su

Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently.

Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval

no code implementations3 Nov 2021 Linlong Xu, Baosong Yang, Xiaoyu Lv, Tianchi Bi, Dayiheng Liu, Haibo Zhang

Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in synchronous and asynchronous fashions, respectively.

Cross-Lingual Information Retrieval Knowledge Distillation +2

POS-Constrained Parallel Decoding for Non-autoregressive Generation

1 code implementation ACL 2021 Kexin Yang, Wenqiang Lei, Dayiheng Liu, Weizhen Qi, Jiancheng Lv

However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation.

Knowledge Distillation POS +2

Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System

no code implementations23 Apr 2021 Cheng Luo, Dayiheng Liu, Chanjuan Li, Li Lu, Jiancheng Lv

The system includes modules such as dialogue topic prediction, knowledge matching and dialogue generation.

Dialogue 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 Natural Questions +1

Let's be Humorous: Knowledge Enhanced Humor Generation

no code implementations ACL 2020 Hang Zhang, Dayiheng Liu, Jiancheng Lv, Cheng Luo

To our knowledge, this is the first attempt to generate punchlines with knowledge enhanced model.

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

Generating Chinese Poetry from Images via Concrete and Abstract Information

no code implementations24 Mar 2020 Yusen Liu, Dayiheng Liu, Jiancheng Lv, Yongsheng Sang

We proposed an infilling-based Chinese poetry generation model which can infill the Concrete keywords into each line of poems in an explicit way, and an abstract information embedding to integrate the Abstract information into generated poems.

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

Deep Poetry: A Chinese Classical Poetry Generation System

no code implementations19 Nov 2019 Yusen Liu, Dayiheng Liu, Jiancheng Lv

For the user's convenience, we deploy the system at the WeChat applet platform, users can use the system on the mobile device whenever and wherever possible.

Deep Learning-Based Automatic Downbeat Tracking: A Brief Review

1 code implementation10 Jun 2019 Bijue Jia, Jiancheng Lv, Dayiheng Liu

Thereinto, downbeat tracking has been a fundamental and continuous problem in Music Information Retrieval (MIR) area.

Feature Engineering Information Retrieval +1

Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning

1 code implementation29 May 2019 Dayiheng Liu, Jie Fu, Yidan Zhang, Chris Pal, Jiancheng Lv

We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer.

Disentanglement Style Transfer +2

TIGS: An Inference Algorithm for Text Infilling with Gradient Search

1 code implementation ACL 2019 Dayiheng Liu, Jie Fu, PengFei Liu, Jiancheng Lv

Text infilling is defined as a task for filling in the missing part of a sentence or paragraph, which is suitable for many real-world natural language generation scenarios.

Text Infilling

mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation

2 code implementations24 May 2019 Dayiheng Liu, Xu Yang, Feng He, YuanYuan Chen, Jiancheng Lv

It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem.

Language Modelling Text Generation

A Multi-Modal Chinese Poetry Generation Model

1 code implementation26 Jun 2018 Dayiheng Liu, Quan Guo, Wubo Li, Jiancheng Lv

Given a picture, the first line, the title and the other lines of the poem are successively generated in three stages.

BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation

no code implementations21 Jun 2018 Dayiheng Liu, Jie Fu, Qian Qu, Jiancheng Lv

Incorporating prior knowledge like lexical constraints into the model's output to generate meaningful and coherent sentences has many applications in dialogue system, machine translation, image captioning, etc.

Image Captioning Machine Translation +1

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