6 citations · 21 across the 5 of their papers we have counts for
7 papers
HilMeMe: A Human-in-the-Loop Machine Translation Evaluation Metric Looking into Multi-Word Expressions
Lifeng Han
With the fast development of Machine Translation (MT) systems, especially the new boost from Neural MT (NMT) models, the MT output quality has reached a new level of accuracy. Howe…
Examining Large Pre-Trained Language Models for Machine Translation: What You Don't Know About It
Lifeng Han, Gleb Erofeev, Irina Sorokina +2
Pre-trained language models (PLMs) often take advantage of the monolingual and multilingual dataset that is freely available online to acquire general or mixed domain knowledge bef…
An Overview on Machine Translation Evaluation
Lifeng Han
Since the 1950s, machine translation (MT) has become one of the important tasks of AI and development, and has experienced several different periods and stages of development, incl…
Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods
Lifeng Han, Gareth J. F. Jones, Alan F. Smeaton
To facilitate effective translation modeling and translation studies, one of the crucial questions to address is how to assess translation quality. From the perspectives of accurac…
Chinese Character Decomposition for Neural MT with Multi-Word Expressions
Lifeng Han, Gareth J. F. Jones, Alan F. Smeaton +1
Chinese character decomposition has been used as a feature to enhance Machine Translation (MT) models, combining radicals into character and word level models. Recent work has inve…
MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
Lifeng Han, Gareth J. F. Jones, Alan F. Smeaton
Multi-word expressions (MWEs) are a hot topic in research in natural language processing (NLP), including topics such as MWE detection, MWE decomposition, and research investigatin…