It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information (2020.acl-main)
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| Challenge: | Current state-of-the-art MT systems are based on neural networks, but it is unclear whether all translation directions are equally easy (or hard) to model for NMT. |
| Approach: | They propose an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models. |
| Outcome: | The proposed metric allows us to better evaluate the difficulty of translating text into the target language while controlling for the difficulty independent of the translation task. |
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