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English–Welsh Cross-Lingual Embeddings
| Content Provider | MDPI |
|---|---|
| Author | Irena, Spasić Espinosa-Anke, Luis Palmer, Geraint Corcoran, Padraig Filimonov, Maxim Knight, Dawn |
| Copyright Year | 2021 |
| Description | Cross-lingual embeddings are vector space representations where word translations tend to be co-located. These representations enable learning transfer across languages, thus bridging the gap between data-rich languages such as English and others. In this paper, we present and evaluate a suite of cross-lingual embeddings for the English–Welsh language pair. To train the bilingual embeddings, a Welsh corpus of approximately 145 M words was combined with an English Wikipedia corpus. We used a bilingual dictionary to frame the problem of learning bilingual mappings as a supervised machine learning task, where a word vector space is first learned independently on a monolingual corpus, after which a linear alignment strategy is applied to map the monolingual embeddings to a common bilingual vector space. Two approaches were used to learn monolingual embeddings, including word2vec and fastText. Three cross-language alignment strategies were explored, including cosine similarity, inverted softmax and cross-domain similarity local scaling (CSLS). We evaluated different combinations of these approaches using two tasks, bilingual dictionary induction, and cross-lingual sentiment analysis. The best results were achieved using monolingual fastText embeddings and the CSLS metric. We also demonstrated that by including a few automatically translated training documents, the performance of a cross-lingual text classifier for Welsh can increase by approximately 20 percent points. |
| Starting Page | 6541 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11146541 |
| Journal | Applied Sciences |
| Issue Number | 14 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-16 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Natural Language Processing Distributional Semantics Machine Learning Language Model Word Embeddings Machine Translation Sentiment Analysis |
| Content Type | Text |
| Resource Type | Article |
