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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 AUTOLEX: An Automatic Framework for Linguistic Exploration ... Chaudhary, Aditi; Sheikh, Zaid; Mortensen, David R. - : arXiv, 2022 BASE Show details 2 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... Pratapa, Adithya; Anastasopoulos, Antonios; Rijhwani, Shruti. - : arXiv, 2021 BASE Show details 3 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 4 AlloVera: A Multilingual Allophone Database ... Mortensen, David R.; Li, Xinjian; Littell, Patrick. - : arXiv, 2020 BASE Show details 5 Automatic Extraction of Rules Governing Morphological Agreement ... Chaudhary, Aditi; Anastasopoulos, Antonios; Pratapa, Adithya. - : arXiv, 2020 BASE Show details 6 Universal Phone Recognition with a Multilingual Allophone System ... Li, Xinjian; Dalmia, Siddharth; Li, Juncheng; Lee, Matthew; Littell, Patrick; Yao, Jiali; Anastasopoulos, Antonios; Mortensen, David R.; Neubig, Graham; Black, Alan W; Metze, Florian. - : arXiv, 2020 Abstract: Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages. Multilingual acoustic models, however, generally ignore the difference between phonemes (sounds that can support lexical contrasts in a particular language) and their corresponding phones (the sounds that are actually spoken, which are language independent). This can lead to performance degradation when combining a variety of training languages, as identically annotated phonemes can actually correspond to several different underlying phonetic realizations. In this work, we propose a joint model of both language-independent phone and language-dependent phoneme distributions. In multilingual ASR experiments over 11 languages, we find that this model improves testing performance by 2% phoneme error rate absolute in low-resource conditions. Additionally, because we are explicitly modeling language-independent phones, we can build a (nearly-)universal phone recognizer that, when ... : ICASSP 2020 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD URL: https://arxiv.org/abs/2002.11800 https://dx.doi.org/10.48550/arxiv.2002.11800 BASE Hide details 7 Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ... Chaudhary, Aditi; Zhou, Chunting; Levin, Lori. - : arXiv, 2018 BASE Show details

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