Tag-Assisted Neural Machine Translation
- Title
- Tag-Assisted Neural Machine Translation
- Authors
- Siekmeier, Aren Michael
- Date Issued
- 2021
- Publisher
- 포항공과대학교
- Abstract
- We implemented a neural machine translation system that uses automatic sequence tagging to improve the quality of translation. Instead of operating on unannotated sentence pairs, our system uses pre-trained tagging systems to add linguistic features to source and target sentences. Our proposed neural architecture learns a combined embedding of tokens and tags in the encoder, and simultaneous token and tag prediction in the decoder. Compared to a baseline with unannotated training, this architecture increased the BLEU score of word-tokenized German to English film subtitle translation outputs by 1.61 points using named entity tags; however, the BLEU score decreased by 0.38 points using part-of-speech tags. This demonstrates that certain token-level tag outputs from off-the-shelf tagging systems can improve the output of neural translation systems using our combined embedding and simultaneous decoding extensions.
- URI
- http://postech.dcollection.net/common/orgView/200000507523
https://oasis.postech.ac.kr/handle/2014.oak/114114
- Article Type
- Thesis
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.