Search Results for author: Lidia S. Chao

Found 55 papers, 19 papers with code

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

Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model

no code implementations18 Mar 2024 Haoyun Xu, Runzhe Zhan, Derek F. Wong, Lidia S. Chao

Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale.

A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions

1 code implementation23 Oct 2023 Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Derek F. Wong, Lidia S. Chao

In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research.

LLM-generated Text Detection Text Detection

Human-in-the-loop Machine Translation with Large Language Model

1 code implementation13 Oct 2023 Xinyi Yang, Runzhe Zhan, Derek F. Wong, Junchao Wu, Lidia S. Chao

The large language model (LLM) has garnered significant attention due to its in-context learning mechanisms and emergent capabilities.

In-Context Learning Language Modelling +5

Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization

1 code implementation3 May 2023 Chi Seng Cheang, Hou Pong Chan, Derek F. Wong, Xuebo Liu, Zhaocong Li, Yanming Sun, Shudong Liu, Lidia S. Chao

Moreover, the knowledge memorized by PLMs may quickly become outdated, which affects the generalization performance of PLMs on future data.

Abstractive Text Summarization

Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation

no code implementations4 Apr 2023 Tao Fang, Shu Yang, Kaixin Lan, Derek F. Wong, Jinpeng Hu, Lidia S. Chao, Yue Zhang

To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT.

Grammatical Error Correction In-Context Learning +2

ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation

1 code implementation8 Dec 2022 Zhaocong Li, Xuebo Liu, Derek F. Wong, Lidia S. Chao, Min Zhang

In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model.

Low-Resource Neural Machine Translation NMT +2

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

Variance-Aware Machine Translation Test Sets

1 code implementation7 Nov 2021 Runzhe Zhan, Xuebo Liu, Derek F. Wong, Lidia S. Chao

We release 70 small and discriminative test sets for machine translation (MT) evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions.

Machine Translation Translation

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation

1 code implementation Findings (EMNLP) 2021 Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).

Machine Translation NMT +2

Difficulty-Aware Machine Translation Evaluation

1 code implementation ACL 2021 Runzhe Zhan, Xuebo Liu, Derek F. Wong, Lidia S. Chao

The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation.

Machine Translation Sentence +1

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning

1 code implementation ICLR 2021 Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Zhaopeng Tu

Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks.

Grammatical Error Correction Machine Translation +3

Document Graph for Neural Machine Translation

no code implementations EMNLP 2021 Mingzhou Xu, Liangyou Li, Derek. F. Wong, Qun Liu, Lidia S. Chao

Previous works have shown that contextual information can improve the performance of neural machine translation (NMT).

Machine Translation NMT +1

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 NMT +2

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 NMT +1

Norm-Based Curriculum Learning for Neural Machine Translation

1 code implementation ACL 2020 Xuebo Liu, Houtim Lai, Derek F. Wong, Lidia S. Chao

We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence.

Machine Translation NMT +2

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

Shared-Private Bilingual Word Embeddings for Neural Machine Translation

no code implementations ACL 2019 Xuebo Liu, Derek F. Wong, Yang Liu, Lidia S. Chao, Tong Xiao, Jingbo Zhu

For similar source and target words, their embeddings tend to share a part of the features and they cooperatively learn these common representation units.

Machine Translation NMT +3

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 Position +1

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

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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