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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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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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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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Disambiguatory Signals are Stronger in Word-initial Positions ...
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Finding Concept-specific Biases in Form--Meaning Associations ...
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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36 |
A Corpus for Large-Scale Phonetic Typology ...
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Abstract:
A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions. We present VoxClamantis V1.0, the first large-scale corpus for phonetic typology, with aligned segments and estimated phoneme-level labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants. Access to such data can greatly facilitate investigation of phonetic typology at a large scale and across many languages. However, it is non-trivial and computationally intensive to obtain such alignments for hundreds of languages, many of which have few to no resources presently available. We describe the methodology to create our corpus, discuss caveats with current methods and their impact on the utility of this data, and illustrate possible research directions through a series of case studies on the 48 highest-quality readings. Our corpus and scripts are publicly available for non-commercial use at https://voxclamantisproject.github.io. ... : Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ...
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URL: http://hdl.handle.net/20.500.11850/446011 https://dx.doi.org/10.3929/ethz-b-000446011
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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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