On Integrating Discourse in Machine Translation
By Karin Sim Smith. 2017
31 citations recorded by the source corpus.
As the quality of Machine Translation (MT) improves, research on improving discourse in automatic translations becomes more viable. This has resulted in an increase in the amount of work on discourse in MT. However many of the existing models and metrics have yet to integrate these insights. Part of this is due to the evaluation methodology, based as it is largely on matching to a single reference. At a time when MT is increasingly being used in a pipeline for other tasks, the semantic element of the translation process needs to be properly integrated into the task. Moreover, in order to take MT to another level, it will need to judge output not based on a single reference translation, but based on notions of fluency and of adequacy – ideally with reference to the source text.
Subfields: Artificial Intelligence, Natural Language Processing
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In Artificial Intelligence
- James Henderson
- Dhananjay Ram
- Nikolaos Pappas
- Lesly Miculicich
- Kellie Webster
- Emily Pitler
- Ivan Titov
- Felix Stahlberg
In Natural Language Processing
- Nikolaos Pappas
- James Henderson
- Lesly Miculicich
- Kellie Webster
- Dhananjay Ram
- Emily Pitler
- Ivan Titov
- Felix Stahlberg
Citations in the corpus
- Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020, methodology): “Recommendations for the use of discourse in translation are developed in Sim Smith (2017).”
- Document-Level Neural Machine Translation with Hierarchical Attention Networks (2018, methodology): “We employed the pre-trained LSA model Wiki-6 from (Stefanescu et al., 2014).”