Search Results for author: Lifeng Han

Found 12 papers, 7 papers with code

An Overview on Machine Translation Evaluation

no code implementations22 Feb 2022 Lifeng Han

Manual evaluation and automatic evaluation include reference-translation based and reference-translation independent participation; automatic evaluation methods include traditional n-gram string matching, models applying syntax and semantics, and deep learning models; evaluation of evaluation methods includes estimating the credibility of human evaluations, the reliability of the automatic evaluation, the reliability of the test set, etc.

Machine Translation Translation

HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation

1 code implementation27 Dec 2021 Serge Gladkoff, Lifeng Han

The initial experimental work carried out on English-Russian language pair MT outputs on marketing content type of text from highly technical domain reveals that our evaluation framework is quite effective in reflecting the MT output quality regarding both overall system-level performance and segment-level transparency, and it increases the IRR for error type interpretation.

Machine Translation Translation

Measuring Uncertainty in Translation Quality Evaluation (TQE)

no code implementations15 Nov 2021 Serge Gladkoff, Irina Sorokina, Lifeng Han, Alexandra Alekseeva

From both human translators (HT) and machine translation (MT) researchers' point of view, translation quality evaluation (TQE) is an essential task.

Machine Translation Translation

cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSE

1 code implementation WMT (EMNLP) 2021 Lifeng Han, Irina Sorokina, Gleb Erofeev, Serge Gladkoff

Then we present the customised hLEPOR (cushLEPOR) which uses Optuna hyper-parameter optimisation framework to fine-tune hLEPOR weighting parameters towards better agreement to pre-trained language models (using LaBSE) regarding the exact MT language pairs that cushLEPOR is deployed to.

Language Modelling

Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods

1 code implementation MoTra (NoDaLiDa) 2021 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.

Machine Translation Natural Language Processing +4

Chinese Character Decomposition for Neural MT with Multi-Word Expressions

1 code implementation NoDaLiDa 2021 Lifeng Han, Gareth J. F. Jones, Alan F. Smeaton, Paolo Bolzoni

To investigate the impact of Chinese decomposition embedding in detail, i. e., radical, stroke, and intermediate levels, and how well these decompositions represent the meaning of the original character sequences, we carry out analysis with both automated and human evaluation of MT.

Machine Translation Translation

AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations

1 code implementation COLING (MWE) 2020 Lifeng Han, Gareth Jones, Alan Smeaton

To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation.

Machine Translation Translation

Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character Level

1 code implementation3 May 2018 Lifeng Han, Shaohui Kuang

We integrate the Chinese radicals into the NMT model with different settings to address the unseen words challenge in Chinese to English translation.

Machine Translation Translation

LEPOR: An Augmented Machine Translation Evaluation Metric

1 code implementation26 Mar 2017 Lifeng Han

Finally, we introduce the practical performance of our metrics in the ACL-WMT workshop shared tasks, which show that the proposed methods are robust across different languages.

Machine Translation Natural Language Processing +2

Machine Translation Evaluation Resources and Methods: A Survey

no code implementations15 May 2016 Lifeng Han

Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT.

Informativeness Machine Translation +3

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