Spencer Rarrick


2019

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Combining Translation Memory with Neural Machine Translation
Akiko Eriguchi | Spencer Rarrick | Hitokazu Matsushita
Proceedings of the 6th Workshop on Asian Translation

In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system’s output.

2011

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Are numbers good enough for you? - A linguistically meaningful MT evaluation method
Takako Aikawa | Spencer Rarrick
Proceedings of Machine Translation Summit XIII: Papers

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MT Detection in Web-Scraped Parallel Corpora
Spencer Rarrick | Chris Quirk | Will Lewis
Proceedings of Machine Translation Summit XIII: Papers