Affiliation:
1. Fudan University, Shanghai, China
2. Fudan University & University of Glasgow, Shanghai, China
3. Independent, Seattle, WA, USA
Abstract
Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-the-art deep learning techniques like Transformer and pre-training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results.
Funder
National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)
Reference61 articles.
1. View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations
2. Multitask Learning for Fine-Grained Twitter Sentiment Analysis
3. Designing for Transient Use
4. Chris Callison-Burch , Miles Osborne , and Philipp Koehn . 2006 . Re-evaluating the role of BLEU in machine translation research . In Proceedings of the 11th Conference of the European Chapter of the Association for Compuational Linguistics. 249--256 . Chris Callison-Burch, Miles Osborne, and Philipp Koehn. 2006. Re-evaluating the role of BLEU in machine translation research. In Proceedings of the 11th Conference of the European Chapter of the Association for Compuational Linguistics. 249--256.
5. Lexically Constrained Neural Machine Translation with Explicit Alignment Guidance
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