Search Results for author: Baosong Yang

Found 37 papers, 11 papers with code

Challenges of Neural Machine Translation for Short Texts

no code implementations CL (ACL) 2022 Yu Wan, Baosong Yang, Derek Fai Wong, Lidia Sam Chao, Liang Yao, Haibo Zhang, Boxing Chen

After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors.

Machine Translation Translation

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

Multi-Hop Transformer for Document-Level Machine Translation

no code implementations NAACL 2021 Long Zhang, Tong Zhang, Haibo Zhang, Baosong Yang, Wei Ye, Shikun Zhang

Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information.

Document Level Machine Translation Document Translation +2

Exploiting Neural Query Translation into Cross Lingual Information Retrieval

no code implementations26 Oct 2020 Liang Yao, Baosong Yang, Haibo Zhang, Weihua Luo, Boxing Chen

As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency.

Cross-Lingual Information Retrieval Data Augmentation +3

Self-Paced Learning for Neural Machine Translation

1 code implementation EMNLP 2020 Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen

Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans.

Machine Translation Translation

Uncertainty-Aware Curriculum Learning for Neural Machine Translation

no code implementations ACL 2020 Yikai Zhou, Baosong Yang, Derek F. Wong, Yu Wan, Lidia S. Chao

We propose uncertainty-aware curriculum learning, which is motivated by the intuition that: 1) the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and 2) the end of the decline in model uncertainty indicates the completeness of current training stage.

Machine Translation Translation

Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling

2 code implementations11 Dec 2019 Yu Wan, Baosong Yang, Derek F. Wong, Lidia S. Chao, Haihua Du, Ben C. H. Ao

As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation.

Machine Translation Translation

Neuron Interaction Based Representation Composition for Neural Machine Translation

no code implementations22 Nov 2019 Jian Li, Xing Wang, Baosong Yang, Shuming Shi, Michael R. Lyu, Zhaopeng Tu

Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e. g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors.

Machine Translation Translation

Assessing the Ability of Self-Attention Networks to Learn Word Order

1 code implementation ACL 2019 Baosong Yang, Long-Yue Wang, Derek F. Wong, Lidia S. Chao, Zhaopeng Tu

Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, e. g. machine translation.

Machine Translation Translation

Information Aggregation for Multi-Head Attention with Routing-by-Agreement

no code implementations NAACL 2019 Jian Li, Baosong Yang, Zi-Yi Dou, Xing Wang, Michael R. Lyu, Zhaopeng Tu

Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces.

Machine Translation Translation

Modeling Recurrence for Transformer

no code implementations NAACL 2019 Jie Hao, Xing Wang, Baosong Yang, Long-Yue Wang, Jinfeng Zhang, Zhaopeng Tu

In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention and recurrent networks.

Machine Translation Translation

Convolutional Self-Attention Networks

no code implementations NAACL 2019 Baosong Yang, Long-Yue Wang, Derek Wong, Lidia S. Chao, Zhaopeng Tu

Self-attention networks (SANs) have drawn increasing interest due to their high parallelization in computation and flexibility in modeling dependencies.

Machine Translation Translation

Context-Aware Self-Attention Networks

no code implementations15 Feb 2019 Baosong Yang, Jian Li, Derek Wong, Lidia S. Chao, Xing Wang, Zhaopeng Tu

Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies.

Translation

Convolutional Self-Attention Network

no code implementations31 Oct 2018 Baosong Yang, Long-Yue Wang, Derek F. Wong, Lidia S. Chao, Zhaopeng Tu

Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies.

Translation

Multi-Head Attention with Disagreement Regularization

no code implementations EMNLP 2018 Jian Li, Zhaopeng Tu, Baosong Yang, Michael R. Lyu, Tong Zhang

Multi-head attention is appealing for the ability to jointly attend to information from different representation subspaces at different positions.

Translation

Towards Bidirectional Hierarchical Representations for Attention-Based Neural Machine Translation

no code implementations EMNLP 2017 Baosong Yang, Derek F. Wong, Tong Xiao, Lidia S. Chao, Jingbo Zhu

This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder.

Machine Translation Translation

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