Alan Smeaton


2021

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Chinese Character Decomposition for Neural MT with Multi-Word Expressions
Lifeng Han | Gareth Jones | Alan Smeaton | Paolo Bolzoni
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

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 investigated ideograph or stroke level embedding. However, questions remain about different decomposition levels of Chinese character representations, radical and strokes, best suited for MT. 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. Furthermore, we investigate if the combination of decomposed Multiword Expressions (MWEs) can enhance the model learning. MWE integration into MT has seen more than a decade of exploration. However, decomposed MWEs has not previously been explored.

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Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods
Lifeng Han | Alan Smeaton | Gareth Jones
Proceedings for the First Workshop on Modelling Translation: Translatology in the Digital Age

2020

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MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
Lifeng Han | Gareth Jones | Alan Smeaton
Proceedings of the 12th Language Resources and Evaluation Conference

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 investigating the exploitation of MWEs in other NLP fields such as Machine Translation. However, the availability of bilingual or multi-lingual MWE corpora is very limited. The only bilingual MWE corpora that we are aware of is from the PARSEME (PARSing and Multi-word Expressions) EU Project. This is a small collection of only 871 pairs of English-German MWEs. In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora. Our collections are 3,159,226 and 143,042 bilingual MWE pairs for German-English and Chinese-English respectively after filtering. We examine the quality of these extracted bilingual MWEs in MT experiments. Our initial experiments applying MWEs in MT show improved translation performances on MWE terms in qualitative analysis and better general evaluation scores in quantitative analysis, on both German-English and Chinese-English language pairs. We follow a standard experimental pipeline to create our MultiMWE corpora which are available online. Researchers can use this free corpus for their own models or use them in a knowledge base as model features.

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AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations
Lifeng Han | Gareth Jones | Alan Smeaton
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually. The languages covered include English, Chinese, Polish, and German. Our original English corpus is taken from the PARSEME shared task in 2018. We performed machine translation of this source corpus followed by human post editing and annotation of target MWEs. Strict quality control was applied for error limitation, i.e., each MT output sentence received first manual post editing and annotation plus second manual quality rechecking. One of our findings during corpora preparation is that accurate translation of MWEs presents challenges to MT systems. To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation. To acquire a broader view of MT issues, we selected four popular state-of-the-art MT models for comparisons namely: Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the noise removal, translation post editing and MWE annotation by human professionals, we believe our AlphaMWE dataset will be an asset for cross-lingual and multilingual research, such as MT and information extraction. Our multilingual corpora are available as open access at github.com/poethan/AlphaMWE.

2014

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Proceedings of the Third Workshop on Vision and Language
Anja Belz | Darren Cosker | Frank Keller | William Smith | Kalina Bontcheva | Sien Moens | Alan Smeaton
Proceedings of the Third Workshop on Vision and Language

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Formulating Queries for Collecting Training Examples in Visual Concept Classification
Kevin McGuinness | Feiyan Hu | Rami Albatal | Alan Smeaton
Proceedings of the Third Workshop on Vision and Language

2011

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On Using Twitter to Monitor Political Sentiment and Predict Election Results
Adam Bermingham | Alan Smeaton
Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011)