| 2 |
Investigating alignment interpretability for low-resource NMT
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Is there a bilingual disadvantage for word segmentation? A computational modeling approach
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
SM to: Is there a bilingual disadvantage for word segmentation? A computational modeling approach ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Early Tashelhiyt Berber word segmentation: the role of the Possible Word Constraint ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Discovering structure in speech recordings: Unsupervised learning of word and phoneme like units for automatic speech recognition
|
|
|
|
In: Fraunhofer IAIS (2021)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Handling cross and out-of-domain samples in Thai word segmentation
|
|
|
|
In: 1003 ; 1016 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Measuring (online) word segmentation in adults and children
|
|
|
|
In: Dutch Journal of Applied Linguistics, Vol 10 (2021) (2021)
|
|
Abstract:
Since Saffran, Aslin and Newport (1996) showed that infants were sensitive to transitional probabilities between syllables after being exposed to a few minutes of fluent speech, there has been ample research on statistical learning. Word segmentation studies usually test learning by making use of “offline methods” such as forced-choice tasks. However, cognitive factors besides statistical learning possibly influence performance on those tasks. The goal of the present study was to improve a method for measuring word segmentation online. Click sounds were added to the speech stream, both between words and within words. Stronger expectations for the next syllable within words as opposed to between words were expected to result in slower detection of clicks within words, revealing sensitivity to word boundaries. Unexpectedly, we did not find evidence for learning in multiple groups of adults and child participants. We discuss possible methodological factors that could have influenced our results.
|
|
Keyword:
click detection task; online measure; P1-1091; Philology. Linguistics; psycholinguistics; statistical learning; word segmentation task
|
|
URL: https://doi.org/10.51751/dujal9607
https://doaj.org/article/a338c87839df4358b7cbdcd23584386d
|
|
BASE
|
|
Hide details
|
|
|
| 9 |
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
F0 Slope and Mean: Cues to Speech Segmentation in French
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Data for: The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 13 |
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Infants Segment Words from Songs—An EEG Study
|
|
|
|
In: Brain Sciences ; Volume 10 ; Issue 1 (2020)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Not all words are equally acquired: transitional probabilities and instructions affect the electrophysiological correlates of statistical learning
|
|
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Controlling Utterance Length in NMT-based Word Segmentation with Attention
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Unsupervised word discovery for computational language documentation ; Découverte non-supervisée de mots pour outiller la linguistique de terrain
|
|
|
|
In: https://tel.archives-ouvertes.fr/tel-02286425 ; Artificial Intelligence [cs.AI]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLS062⟩ (2019)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language
|
|
|
|
In: Information ; Volume 10 ; Issue 10 (2019)
|
|
BASE
|
|
Show details
|
|
|
|
|
