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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information ...
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Abstract:
The performance of neural machine translation systems is commonly evaluated in terms of BLEU. However, due to its reliance on target language properties and generation, the BLEU metric does not allow an assessment of which translation directions are more difficult to model. In this paper, we propose cross-mutual information (XMI): an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models. XMI allows us to better evaluate the difficulty of translating text into the target language while controlling for the difficulty of the target-side generation component independent of the translation task. We then present the first systematic and controlled study of cross-lingual translation difficulties using modern neural translation systems. Code for replicating our experiments is available online at https://github.com/e-bug/nmt-difficulty. ... : Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ...
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URL: https://dx.doi.org/10.3929/ethz-b-000462309 http://hdl.handle.net/20.500.11850/462891
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Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing ...
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94 |
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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