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DeepL et Google Translate face à l'ambiguïté phraséologique
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In: https://hal.archives-ouvertes.fr/hal-03583995 ; 2022 (2022)
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VEREINDEUTIGUNG ZUR KLASSIFIZIERUNG LEXIKALISCHER OBJEKTE ; DISAMBIGUATION FOR THE CLASSIFICATION OF LEXICAL ITEMS ; DÉSAMBÏGUISATION POUR LA CLASSIFICATION DE LEXÈMES
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In: https://hal.archives-ouvertes.fr/hal-03598242 ; France, Patent n° : EP3937059A1. 2022 (2022)
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A New Amharic Speech Emotion Dataset and Classification Benchmark ...
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LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects ...
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Automatic Dialect Density Estimation for African American English ...
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Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition ...
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Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language ...
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Improving Persian Relation Extraction Models by Data Augmentation ...
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Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models ...
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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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Politics and Virality in the Time of Twitter: A Large-Scale Cross-Party Sentiment Analysis in Greece, Spain and United Kingdom ...
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Tackling data scarcity in speech translation using zero-shot multilingual machine translation techniques ...
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A new approach to calculating BERTScore for automatic assessment of translation quality ...
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Emergent Communication for Understanding Human Language Evolution: What's Missing? ...
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A Feasibility Study of Answer-Agnostic Question Generation for Education ...
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Computational historical linguistics and language diversity in South Asia ...
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Estimating the Entropy of Linguistic Distributions ...
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Abstract:
Shannon entropy is often a quantity of interest to linguists studying the communicative capacity of human language. However, entropy must typically be estimated from observed data because researchers do not have access to the underlying probability distribution that gives rise to these data. While entropy estimation is a well-studied problem in other fields, there is not yet a comprehensive exploration of the efficacy of entropy estimators for use with linguistic data. In this work, we fill this void, studying the empirical effectiveness of different entropy estimators for linguistic distributions. In a replication of two recent information-theoretic linguistic studies, we find evidence that the reported effect size is over-estimated due to over-reliance on poor entropy estimators. Finally, we end our paper with concrete recommendations for entropy estimation depending on distribution type and data availability. ... : 21 pages (5 pages main text). 4 figures. Accepted to ACL 2022 ...
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Keyword:
94A17 Primary 62B10 Secondary; Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; E.4
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URL: https://arxiv.org/abs/2204.01469 https://dx.doi.org/10.48550/arxiv.2204.01469
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A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots ...
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