DE eng

Search in the Catalogues and Directories

Hits 1 – 15 of 15

1
XTREME-S: Evaluating Cross-lingual Speech Representations ...
BASE
Show details
2
mSLAM: Massively multilingual joint pre-training for speech and text ...
Bapna, Ankur; Cherry, Colin; Zhang, Yu. - : arXiv, 2022
BASE
Show details
3
Larger-Scale Transformers for Multilingual Masked Language Modeling ...
Goyal, Naman; Du, Jingfei; Ott, Myle. - : arXiv, 2021
BASE
Show details
4
Multilingual Speech Translation from Efficient Finetuning of Pretrained Models ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.68 Abstract: We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning 10~50% of the pretrained parameters. This effectively leverages large pretrained models at low training cost such as wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation. This sets a new state-of-the-art for 36 translation directions (and surpassing cascaded ST for 26 of them) on the large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average for En-X directions and +6.7 BLEU for X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.6 BLEU on average across 28 non-English directions), making it an ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/z2bj-rv03
https://underline.io/lecture/25429-multilingual-speech-translation-from-efficient-finetuning-of-pretrained-models
BASE
Hide details
5
Unsupervised Cross-lingual Representation Learning for Speech Recognition ...
BASE
Show details
6
Multilingual Speech Translation with Efficient Finetuning of Pretrained Models ...
Li, Xian; Wang, Changhan; Tang, Yun. - : arXiv, 2020
BASE
Show details
7
Unsupervised Cross-lingual Representation Learning at Scale ...
BASE
Show details
8
Emerging Cross-lingual Structure in Pretrained Language Models ...
BASE
Show details
9
Specializing distributional vectors of all words for lexical entailment
Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019
BASE
Show details
10
What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01898412 ; ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.2126-2136 (2018)
BASE
Show details
11
XNLI: Evaluating Cross-lingual Sentence Representations ...
BASE
Show details
12
What you can cram into a single vector: Probing sentence embeddings for linguistic properties ...
BASE
Show details
13
Very Deep Convolutional Networks for Text Classification
In: European Chapter of the Association for Computational Linguistics EACL'17 ; https://hal.archives-ouvertes.fr/hal-01454940 ; European Chapter of the Association for Computational Linguistics EACL'17, 2017, Valencia, Spain (2017)
BASE
Show details
14
Word Translation Without Parallel Data ...
BASE
Show details
15
What you can cram into a single $&!#* vector: probing sentence embeddings for linguistic properties
Kruszewski, German; Barrault, Loïc; Baroni, Marco. - : ACL (Association for Computational Linguistics)
BASE
Show details

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
15
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern