Issue #4 – Six Challenges in Neural MT
08 Aug18
Issue #4 – Six Challenges in Neural MT
Author: Dr. John Tinsley, CEO @ Iconic
A little over a year ago, Koehn and Knowles (2017) wrote a very appropriate paper entitled “Six Challenges in Neural Machine Translation” (in fact, there were 7 but only 6 were empirically tested). The paper set out a number of areas which, despite its rapid development, still needed to be addressed by researchers and developers of Neural MT. The seven challenges posed at the time were:
- Translating out-of-domain data
- The need for a lot of training data
- Translating rare / unknown words
- Handling of long sentences
- No word alignments
- Inconsistency at decoding time
- Results are not very interpretable
In this post, we take a look at the practical implications of each of these challenges on commercial applications of Neural MT, and note where progress has been made over the past 12 months.
1. Translating out-of-domain data
This is a “traditional” problem for MT, that is exacerbated by Neural MT’s sensitivity to different types of data. A practical implication here is that engines may not be as robust to use across different domains/content types. Therefore, customisation for more narrower use cases may be needed. This also makes the
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