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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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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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In: INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (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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MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible
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In: Proceedings of The 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02611059 ; Proceedings of The 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.6486 - 6493 (2020)
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Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
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In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
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How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
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In: Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT). ; https://hal.archives-ouvertes.fr/hal-02895895 ; Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT)., Nov 2019, Orléans, France (2019)
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A small Griko-Italian speech translation corpus
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In: 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18) ; https://hal.archives-ouvertes.fr/hal-01962528 ; 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18), Aug 2018, New Delhi, India (2018)
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Unsupervised Word Segmentation from Speech with Attention
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In: Interspeech 2018 ; https://hal.archives-ouvertes.fr/hal-01818092 ; Interspeech 2018, Sep 2018, Hyderabad, India (2018)
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Unsupervised Word Segmentation from Speech with Attention ...
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Unwritten Languages Demand Attention Too! Word Discovery with Encoder-Decoder Models
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In: IEEE Automatic Speech Recognition and Understanding (ASRU) ; https://hal.archives-ouvertes.fr/hal-01592091 ; IEEE Automatic Speech Recognition and Understanding (ASRU), Dec 2017, Okinawa, Japan (2017)
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Unsupervised Word Discovery Using Attentional Encoder-Decoder Models
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In: WiNLP workshop, ACL 2017 ; https://hal.archives-ouvertes.fr/hal-02895851 ; WiNLP workshop, ACL 2017, Jul 2017, Vancouver, Canada (2017)
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Abstract:
International audience ; Attention-based sequence-to-sequence neural machine translation systems have been shown to jointly align and translate source sentences into target sentences. In this project we use unsegmented symbol sequences (characters and phonemes) as source, aiming to explore the soft-alignment probability matrices generated during training and to evaluate if these soft-alignments allow us to discover latent lexicon representations. If successful, such approach could be useful for documenting unwritten and/or endangered languages. However, for this to be feasible, attention models should be robust to low-resource scenarios, of several thousand of sentences only. We use a parallel corpus between the endangered language Mboshi and French, as well as a larger and more controlled English-French parallel corpus. Our goal is to explore different representation levels and study their impact, together with the impact of different data set sizes, in the quality of the generated soft-alignment probability matrices.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]
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URL: https://hal.archives-ouvertes.fr/hal-02895851/file/44_Paper.pdf https://hal.archives-ouvertes.fr/hal-02895851/document https://hal.archives-ouvertes.fr/hal-02895851
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