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Investigating alignment interpretability for low-resource NMT
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In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021)
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Is there a bilingual disadvantage for word segmentation? A computational modeling approach
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In: ISSN: 0305-0009 ; EISSN: 1469-7602 ; Journal of Child Language ; https://hal.archives-ouvertes.fr/hal-03498905 ; Journal of Child Language, Cambridge University Press (CUP), 2021, pp.1-28. ⟨10.1017/S0305000921000568⟩ (2021)
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Abstract:
International audience ; Abstract Since there are no systematic pauses delimiting words in speech, the problem of word segmentation is formidable even for monolingual infants. We use computational modeling to assess whether word segmentation is substantially harder in a bilingual than a monolingual setting. Seven algorithms representing different cognitive approaches to segmentation are applied to transcriptions of naturalistic input to young children, carefully processed to generate perfectly matched monolingual and bilingual corpora. We vary the overlap in phonology and lexicon experienced by modeling exposure to languages that are more similar (Catalan and Spanish) or more different (English and Spanish). We find that the greatest variation in performance is due to different segmentation algorithms and the second greatest to language, with bilingualism having effects that are smaller than both algorithm and language effects. Implications of these computational results for experimental and modeling approaches to language acquisition are discussed.
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Keyword:
[SCCO.LING]Cognitive science/Linguistics; [SCCO.PSYC]Cognitive science/Psychology; computational modeling; infancy; word segmentation
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URL: https://doi.org/10.1017/S0305000921000568
https://hal.archives-ouvertes.fr/hal-03498905/file/2021_Fibla_JCL.pdf
https://hal.archives-ouvertes.fr/hal-03498905/document
https://hal.archives-ouvertes.fr/hal-03498905
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SM to: Is there a bilingual disadvantage for word segmentation? A computational modeling approach ...
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Early Tashelhiyt Berber word segmentation: the role of the Possible Word Constraint ...
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Discovering structure in speech recordings: Unsupervised learning of word and phoneme like units for automatic speech recognition
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In: Fraunhofer IAIS (2021)
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Handling cross and out-of-domain samples in Thai word segmentation
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In: 1003 ; 1016 (2021)
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Measuring (online) word segmentation in adults and children
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In: Dutch Journal of Applied Linguistics, Vol 10 (2021) (2021)
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Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
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In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020)
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F0 Slope and Mean: Cues to Speech Segmentation in French
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In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-03042331 ; Interspeech 2020, Oct 2020, Shanghai, China. pp.1610-1614, ⟨10.21437/Interspeech.2020-2509⟩ (2020)
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| 11 |
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
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Data for: The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
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The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
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Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech
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Infants Segment Words from Songs—An EEG Study
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In: Brain Sciences ; Volume 10 ; Issue 1 (2020)
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Not all words are equally acquired: transitional probabilities and instructions affect the electrophysiological correlates of statistical learning
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Controlling Utterance Length in NMT-based Word Segmentation with Attention
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In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-02343206 ; International Workshop on Spoken Language Translation, Nov 2019, Hong-Kong, China (2019)
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Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus
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In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-02274050 ; Open Mind, MIT Press, 2019, 3, pp.13-22. ⟨10.1162/opmi_a_00022⟩ (2019)
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Unsupervised word discovery for computational language documentation ; Découverte non-supervisée de mots pour outiller la linguistique de terrain
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In: https://tel.archives-ouvertes.fr/tel-02286425 ; Artificial Intelligence [cs.AI]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLS062⟩ (2019)
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MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language
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In: Information ; Volume 10 ; Issue 10 (2019)
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