activity
20162024
most citedNeural Machine Translation with Supervised Attention

27 citations · 36 across the 16 of their papers we have counts for

collaborators

10 papers

cs.CL2023

Context Consistency between Training and Testing in Simultaneous Machine Translation

Meizhi Zhong, Lemao Liu, Kehai Chen +2

Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context. However, there is a counterintuitive phe…

cs.CL2023

Rethinking Word-Level Auto-Completion in Computer-Aided Translation

Xingyu Chen, Lemao Liu, Guoping Huang +4

Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation. It aims at providing word-level auto-completion suggestions for human translators. While pr…

cs.CL2023

Towards General Error Diagnosis via Behavioral Testing in Machine Translation

Junjie Wu, Lemao Liu, Dit-Yan Yeung

Behavioral testing offers a crucial means of diagnosing linguistic errors and assessing capabilities of NLP models. However, applying behavioral testing to machine translation (MT)…

cs.CL2023

IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems

Xu Huang, Zhirui Zhang, Ruize Gao +6

We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art model…

cs.CL20233 cited

Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective

Huayang Li, Tian Lan, Zihao Fu +5

There are a number of diverging hypotheses about the neural text degeneration problem, i.e., generating repetitive and dull loops, which makes this problem both interesting and con…

cs.CL2023

Rethinking Translation Memory Augmented Neural Machine Translation

Hongkun Hao, Guoping Huang, Lemao Liu +3

This paper rethinks translation memory augmented neural machine translation (TM-augmented NMT) from two perspectives, i.e., a probabilistic view of retrieval and the variance-bias…