244 citations · 467 across the 37 of their papers we have counts for
39 papers · 1 filter
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation
Cheng Yang, Guoping Huang, Mo Yu +6
Word-level AutoCompletion(WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neura…
Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks
Yifan Wang, Qingyan Guo, Xinzhe Ni +4
In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform…
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…
On Synthetic Data for Back Translation
Jiahao Xu, Yubin Ruan, Wei Bi +4
Back translation (BT) is one of the most significant technologies in NMT research fields. Existing attempts on BT share a common characteristic: they employ either beam search or r…
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…
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…