1 |
Segmentation en mots faiblement supervisée pour la documentation automatique des langues
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03477475 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
2 |
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Lightweight Adapter Tuning for Multilingual Speech Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis
|
|
|
|
In: ComputEL-4: Fourth Workshop on the Use of Computational Methods in the Study of Endangered Languages ; https://halshs.archives-ouvertes.fr/halshs-03030529 ; 2020 ; https://computel-workshop.org/ (2020)
|
|
BASE
|
|
Show details
|
|
7 |
A Data Efficient End-To-End Spoken Language Understanding Architecture ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech ...
|
|
|
|
Abstract:
The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units. This suggests that the ideal way of learning a language is by starting from full semantic units. In this paper, we investigate if this is also the case for a neural model of Visually Grounded Speech trained on a speech-image retrieval task. We evaluated how well such a network is able to learn a reliable speech-to-image mapping when provided with phone, syllable, or word boundary information. We present a simple way to introduce such information into an RNN-based model and investigate which type of boundary is the most efficient. We also explore at which level of the network's architecture such information should be introduced so as to maximise its performances. Finally, we show that using multiple boundary types at once in a ... : Accepted at CoNLL20 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.2006.08387 https://arxiv.org/abs/2006.08387
|
|
BASE
|
|
Hide details
|
|
9 |
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Controlling Utterance Length in NMT-based Word Segmentation with Attention ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
MaSS - Multilingual corpus of Sentence-aligned Spoken utterances ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
MaSS - Multilingual corpus of Sentence-aligned Spoken utterances ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Models of Visually Grounded Speech Signal Pay Attention To Nouns: a Bilingual Experiment on English and Japanese ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Unsupervised Word Segmentation from Speech with Attention ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|