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Differentiable subset pruning of transformer heads
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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62 |
Parameter space factorization for zero-shot learning across tasks and languages
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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63 |
Disambiguatory Signals are Stronger in Word-initial Positions
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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
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64 |
Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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65 |
How (Non-)Optimal is the Lexicon?
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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66 |
A Bayesian Framework for Information-Theoretic Probing
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Abstract:
Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents—allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task.
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URL: https://doi.org/10.3929/ethz-b-000518995 https://hdl.handle.net/20.500.11850/518995
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67 |
Examining the Inductive Bias of Neural Language Models with Artificial Languages
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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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68 |
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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71 |
Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing ...
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72 |
Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing ...
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76 |
Disambiguatory Signals are Stronger in Word-initial Positions ...
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77 |
Finding Concept-specific Biases in Form--Meaning Associations ...
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78 |
Backtranslation feedback improves user confidence in MT, not quality
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79 |
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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