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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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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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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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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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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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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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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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70 |
A Cognitive Regularizer for Language Modeling ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.404 Abstract: The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across a linguistic signal, has gained traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices. In this work, we explore whether the UID hypothesis can be operationalized as an inductive bias for statistical language modeling. Specifically, we augment the canonical MLE objective for training language models with a regularizer that encodes UID. In experiments on ten languages spanning five language families, we find that using UID regularization consistently improves perplexity in language models, having a larger effect when training data is limited. Moreover, via an analysis of generated sequences, we find that UID-regularized language models have other desirable properties, e.g., they generate text that is more lexically diverse. Our results not only ...
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Keyword:
Cognitive Linguistics; Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://underline.io/lecture/25822-a-cognitive-regularizer-for-language-modeling https://dx.doi.org/10.48448/y299-yz80
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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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