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A Non-Linear Structural Probe
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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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22 |
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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23 |
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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24 |
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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Disambiguatory Signals are Stronger in Word-initial Positions ...
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29 |
Finding Concept-specific Biases in Form--Meaning Associations ...
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30 |
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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34 |
Phonotactic Complexity and its Trade-offs ...
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Abstract:
We present methods for calculating a measure of phonotactic complexity---bits per phoneme---that permits a straightforward cross-linguistic comparison. When given a word, represented as a sequence of phonemic segments such as symbols in the international phonetic alphabet, and a statistical model trained on a sample of word types from the language, we can approximately measure bits per phoneme using the negative log-probability of that word under the model. This simple measure allows us to compare the entropy across languages, giving insight into how complex a language's phonotactics are. Using a collection of 1016 basic concept words across 106 languages, we demonstrate a very strong negative correlation of -0.74 between bits per phoneme and the average length of words. ... : Published in TACL: https://doi.org/10.1162/tacl_a_00296 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2005.03774 https://dx.doi.org/10.48550/arxiv.2005.03774
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40 |
Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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