| 1 |
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
|
|
|
|
In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
|
|
|
|
In: https://hal.inria.fr/hal-03161685 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi
|
|
|
|
In: https://hal.inria.fr/hal-03161677 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
|
|
|
|
In: EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03239087 ; EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2021, Kyiv / Virtual, Ukraine ; https://2021.eacl.org/ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
|
|
|
|
In: NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.inria.fr/hal-03251105 ; NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2021, Mexico City, Mexico (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
|
|
|
|
In: https://hal.inria.fr/hal-03109187 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
|
|
|
|
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889804 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, Canada. ⟨10.18653/v1/2020.acl-main.107⟩ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
CamemBERT: a Tasty French Language Model
|
|
|
|
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889805 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. ⟨10.18653/v1/2020.acl-main.645⟩ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
|
|
|
|
In: https://hal.inria.fr/hal-03109106 ; 2020 (2020)
|
|
Abstract:
Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. Transliterating those languages improves very significantly the ability of large-scale multilingual language models on downstream tasks.
|
|
Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
|
|
URL: https://hal.inria.fr/hal-03109106
|
|
BASE
|
|
Hide details
|
|
|
| 17 |
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 18 |
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
CamemBERT: a Tasty French Language Model
|
|
|
|
In: https://hal.inria.fr/hal-02445946 ; 2019 (2019)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Enhancing BERT for Lexical Normalization
|
|
|
|
In: The 5th Workshop on Noisy User-generated Text (W-NUT) ; https://hal.inria.fr/hal-02294316 ; The 5th Workshop on Noisy User-generated Text (W-NUT), Nov 2019, Hong Kong, China (2019)
|
|
BASE
|
|
Show details
|
|
|
|
|
