59 citations · 177 across the 21 of their papers we have counts for
21 papers
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm
Hexuan Deng, Xin Zhang, Meishan Zhang +2
In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for UDS parsing that decomposes the com…
Pluggable Neural Machine Translation Models via Memory-augmented Adapters
Yuzhuang Xu, Shuo Wang, Peng Li +4
Although neural machine translation (NMT) models perform well in the general domain, it remains rather challenging to control their generation behavior to satisfy the requirement o…
Revisiting Token Dropping Strategy in Efficient BERT Pretraining
Qihuang Zhong, Liang Ding, Juhua Liu +4
Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at s…
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Chi Seng Cheang, Hou Pong Chan, Derek F. Wong +5
Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with…
Towards Making the Most of ChatGPT for Machine Translation
Keqin Peng, Liang Ding, Qihuang Zhong +5
ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource lan…
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
Zhaocong Li, Xuebo Liu, Derek F. Wong +2
Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods f…
Improving Simultaneous Machine Translation with Monolingual Data
Hexuan Deng, Liang Ding, Xuebo Liu +3
Simultaneous machine translation (SiMT) is usually done via sequence-level knowledge distillation (Seq-KD) from a full-sentence neural machine translation (NMT) model. However, the…
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling
Zhijun Wang, Xuebo Liu, Min Zhang
Existing research generally treats Chinese character as a minimum unit for representation. However, such Chinese character representation will suffer two bottlenecks: 1) Learning b…
Revisiting Grammatical Error Correction Evaluation and Beyond
Peiyuan Gong, Xuebo Liu, Heyan Huang +1
Pretraining-based (PT-based) automatic evaluation metrics (e.g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e.g., machine translation and…
BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation
Zheng Zhang, Liang Ding, Dazhao Cheng +3
Data augmentations (DA) are the cores to achieving robust sequence-to-sequence learning on various natural language processing (NLP) tasks. However, most of the DA approaches force…
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Bei Li, Quan Du, Tao Zhou +7
Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE…
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
Runzhe Zhan, Xuebo Liu, Derek F. Wong +1
Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the transl…
Understanding and Improving Lexical Choice in Non-Autoregressive Translation
Liang Ding, Longyue Wang, Xuebo Liu +3
Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT) models by reducing the complexity of the raw data with an autoregressive teacher model. I…
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
Xuebo Liu, Longyue Wang, Derek F. Wong +3
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 effe…
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding
Zilong Wang, Mingjie Zhan, Xuebo Liu +1
Form understanding depends on both textual contents and organizational structure. Although modern OCR performs well, it is still challenging to realize general form understanding b…
AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting
Wenhai Wang, Xuebo Liu, Xiaozhong Ji +6
Scene text spotting aims to detect and recognize the entire word or sentence with multiple characters in natural images. It is still challenging because ambiguity often occurs when…
Norm-Based Curriculum Learning for Neural Machine Translation
Xuebo Liu, Houtim Lai, Derek F. Wong +1
A neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and…
Scene Text Image Super-Resolution in the Wild
Wenjia Wang, Enze Xie, Xuebo Liu +4
Low-resolution text images are often seen in natural scenes such as documents captured by mobile phones. Recognizing low-resolution text images is challenging because they lose det…
PolarMask: Single Shot Instance Segmentation with Polar Representation
Enze Xie, Peize Sun, Xiaoge Song +4
In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction…
Shared-Private Bilingual Word Embeddings for Neural Machine Translation
Xuebo Liu, Derek F. Wong, Yang Liu +3
Word embedding is central to neural machine translation (NMT), which has attracted intensive research interest in recent years. In NMT, the source embedding plays the role of the e…
Dynamic Multi-path Neural Network
Yingcheng Su, Shunfeng Zhou, Yichao Wu +7
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resourc…