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SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding ...
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Improving Tokenisation by Alternative Treatment of Spaces ...
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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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Abstract:
International audience ; The attention mechanism in Neural Machine Translation (NMT) models added flexibility to translation systems, and the possibility to visualize soft-alignments between source and target representations. While there is much debate about the relationship between attention and the yielded output for neural models [26, 35, 43, 38], in this paper we propose a different assessment, investigating soft-alignment interpretability in low-resource scenarios. We experimented with different architectures (RNN [5], 2D-CNN [15], and Transformer [39]), comparing them with regards to their ability to produce directly exploitable alignments. For evaluating exploitability, we replicated the Unsupervised Word Segmentation (UWS) task from Godard et al. [22]. There, source words are translated into unsegmented phone sequences. Posterior to training, the resulting soft-alignments are used for producing segmentation over the target side. Our results showed that a RNN-based NMT model produced the most exploitable alignments in this scenario. We then investigated methods for increasing its UWS scores by comparing the following methodologies: monolingual pre-training, input representation augmentation (hybrid model), and explicit word length optimization during training. We reached the best results by using the hybrid model, which uses an intermediate monolingual-rooted segmentation from a non-parametric Bayesian model [25] to enrich the input representation before training.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; attention mechanism; computational language documentation; low-resource languages; neural machine translation; sequence-tosequence models; unsupervised word segmentation
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URL: https://doi.org/10.1007/s10590-020-09254-w https://hal.archives-ouvertes.fr/hal-03139744
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AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models ...
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Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings ...
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Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels ...
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The Role of negative information when learning dense word vectors ; O papel da informação negativa na aprendizagem de vetores palavra densos
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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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Annotated corpora and tools of the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)
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Investigating Language Impact in Bilingual Approaches for Computational Language Documentation ...
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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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Unsupervised Compositionality Prediction of Nominal Compounds
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02318196 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (1), pp.1-57. ⟨10.1162/coli_a_00341⟩ (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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How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages ...
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CogniVal: A Framework for Cognitive Word Embedding Evaluation
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In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) (2019)
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Unsupervised Compositionality Prediction of Nominal Compounds
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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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Language, Cognition, and Computational Models
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In: https://hal.archives-ouvertes.fr/hal-01722351 ; Cambridge University Press, 2018 ; https://www.cambridge.org/core/books/language-cognition-and-computational-models/90CC7DBA6CADB1FE361266D311CB4413 (2018)
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