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Automatic Dialect Density Estimation for African American English ...
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Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection ...
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Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition ...
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End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system ...
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Large-scale Bilingual Language-Image Contrastive Learning ...
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Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
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Semantic properties of English nominal pluralization: Insights from word embeddings ...
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Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns ...
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NorDiaChange: Diachronic Semantic Change Dataset for Norwegian ...
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Toxicity Detection for Indic Multilingual Social Media Content ...
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Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks ...
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SciNLI: A Corpus for Natural Language Inference on Scientific Text ...
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SHAS: Approaching optimal Segmentation for End-to-End Speech Translation ...
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A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns ...
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Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese ...
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Abstract:
Coordinate compounds (CCs) and elaborate expressions (EEs) are coordinate constructions common in languages of East and Southeast Asia. Mortensen (2006) claims that (1) the linear ordering of EEs and CCs in Hmong, Lahu, and Chinese can be predicted via phonological hierarchies and (2) these phonological hierarchies lack a clear phonetic rationale. These claims are significant because morphosyntax has often been seen as in a feed-forward relationship with phonology, and phonological generalizations have often been assumed to be phonetically "natural". We investigate whether the ordering of CCs and EEs can be learned empirically and whether computational models (classifiers and sequence labeling models) learn unnatural hierarchies similar to those posited by Mortensen (2006). We find that decision trees and SVMs learn to predict the order of CCs/EEs on the basis of phonology, with DTs learning hierarchies strikingly similar to those proposed by Mortensen. However, we also find that a neural sequence labeling ... : To be published in NAACL2022 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2204.04080 https://dx.doi.org/10.48550/arxiv.2204.04080
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Learning to pronounce as measuring cross-lingual joint orthography-phonology complexity ...
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The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking ...
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