MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
This provides a valuable resource for NLP researchers working on MWE-related tasks like machine translation, though it is incremental as it builds on existing extraction methods.
The authors tackled the limited availability of bilingual or multi-lingual multi-word expression (MWE) corpora by extracting large-scale parallel datasets from root parallel corpora, resulting in 3,159,226 German-English and 143,042 Chinese-English MWE pairs, and initial machine translation experiments showed improved performance on MWE terms and better general evaluation scores.
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.