Evaluating Discourse Phenomena in Neural Machine Translation

@inproceedings{Bawden2018EvaluatingDP,
  title={Evaluating Discourse Phenomena in Neural Machine Translation},
  author={Rachel Bawden and Rico Sennrich and Alexandra Birch and B. Haddow},
  booktitle={NAACL-HLT},
  year={2018}
}
For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate… Expand
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References

SHOWING 1-10 OF 27 REFERENCES
Exploiting Cross-Sentence Context for Neural Machine Translation
A Quantitative Analysis of Discourse Phenomena in Machine Translation
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation with Extended Context
A Challenge Set Approach to Evaluating Machine Translation
Neural Machine Translation of Rare Words with Subword Units
Multimodal Attention for Neural Machine Translation
Does Neural Machine Translation Benefit from Larger Context?
...
1
2
3
...