Statistical machine translation customization between turkish and 11 languages
Statistical Machine Translation (SMT) has been the dominant corpus-based machine translation (MT) approach in the last twenty years. While SMT has been studied in detail among European languages, it has not been studied sufficiently in language pairs including Turkish as source or target language, a...
| Author: | |
|---|---|
| Format: | Article |
| Status: | Published version |
| Publication date: | 2020 |
| Country: | España |
| Institution: | Universitat Pompeu Fabra |
| Repository: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/69351 |
| Online Access: | http://hdl.handle.net/10230/69351 http://dx.doi.org/10.29228/transLogos.23 |
| Access Level: | Open access |
| Keyword: | Statistical machine translation customization Turkish Automatic evaluation metrics Translation quality evaluation Parallel corpus |
| _version_ | 1878733762490007552 |
|---|---|
| author | Dogru, Gokhan |
| author_browse | Dogru, Gokhan |
| author_facet | Dogru, Gokhan |
| author_role | author |
| collection | Repositorio Digital de la UPF |
| description | Statistical Machine Translation (SMT) has been the dominant corpus-based machine translation (MT) approach in the last twenty years. While SMT has been studied in detail among European languages, it has not been studied sufficiently in language pairs including Turkish as source or target language, and its study has been limited mostly to English ↔ Turkish language pair. This study aims to broaden the perspective on Turkish corpus-based MT studies by training MT engines between Turkish and a wide variety of languages with different features. It surveys customized SMT between Turkish and 11 different languages. Twenty-two SMT engines have been trained in KantanMT with open parallel corpora using Turkish as both source and target language. Three automatic evaluation metrics F-Measure, BLEU, and TER have been used for evaluating MT quality. Due to the variations in the corpus quality and size, highly varying results have been achieved. While Turkish ↔ Catalan engines have had the highest automatic evaluation scores, Turkish ↔ Arabic engines have had the lowest automatic scores. While the quality results are highly varying across languages, we obtain baseline scores for a wide variety of languages coupled with Turkish. These results may provide a reference point for evaluating future MT systems including Turkish. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_5c7d30ea3c68f6d55c3a483ca27b6501 |
| institution | Universitat Pompeu Fabra |
| instname_str | Universitat Pompeu Fabra |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:repositori.upf.edu:10230/69351 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| publisher | Diye Global Communications |
| publisherStr | Diye Global Communications |
| reponame_str | Repositorio Digital de la UPF |
| spelling | Statistical machine translation customization between turkish and 11 languages Dogru, Gokhan Statistical machine translation customization Turkish Automatic evaluation metrics Translation quality evaluation Parallel corpus Statistical Machine Translation (SMT) has been the dominant corpus-based machine translation (MT) approach in the last twenty years. While SMT has been studied in detail among European languages, it has not been studied sufficiently in language pairs including Turkish as source or target language, and its study has been limited mostly to English ↔ Turkish language pair. This study aims to broaden the perspective on Turkish corpus-based MT studies by training MT engines between Turkish and a wide variety of languages with different features. It surveys customized SMT between Turkish and 11 different languages. Twenty-two SMT engines have been trained in KantanMT with open parallel corpora using Turkish as both source and target language. Three automatic evaluation metrics F-Measure, BLEU, and TER have been used for evaluating MT quality. Due to the variations in the corpus quality and size, highly varying results have been achieved. While Turkish ↔ Catalan engines have had the highest automatic evaluation scores, Turkish ↔ Arabic engines have had the lowest automatic scores. While the quality results are highly varying across languages, we obtain baseline scores for a wide variety of languages coupled with Turkish. These results may provide a reference point for evaluating future MT systems including Turkish. Diye Global Communications http://hdl.handle.net/10230/69351 http://dx.doi.org/10.29228/transLogos.23 |
| spellingShingle | Statistical machine translation customization between turkish and 11 languages Dogru, Gokhan Statistical machine translation customization Turkish Automatic evaluation metrics Translation quality evaluation Parallel corpus |
| status_str | publishedVersion |
| title | Statistical machine translation customization between turkish and 11 languages |
| title_full | Statistical machine translation customization between turkish and 11 languages |
| title_fullStr | Statistical machine translation customization between turkish and 11 languages |
| title_full_unstemmed | Statistical machine translation customization between turkish and 11 languages |
| title_short | Statistical machine translation customization between turkish and 11 languages |
| title_sort | Statistical machine translation customization between turkish and 11 languages |
| topic | Statistical machine translation customization Turkish Automatic evaluation metrics Translation quality evaluation Parallel corpus |
| topic_facet | Statistical machine translation customization Turkish Automatic evaluation metrics Translation quality evaluation Parallel corpus |
| url | http://hdl.handle.net/10230/69351 http://dx.doi.org/10.29228/transLogos.23 |