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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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Intrinsic Probing through Dimension Selection ...
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Abstract:
Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted. To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal. We then probe fastText and BERT for various morphosyntactic attributes across 36 languages. We find that most attributes are reliably encoded by only a few neurons, with fastText ... : Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ...
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URL: http://hdl.handle.net/20.500.11850/462314 https://dx.doi.org/10.3929/ethz-b-000462314
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89 |
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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