Cross-lingual Lifelong Learning
@article{Mhamdi2022CrosslingualLL, title={Cross-lingual Lifelong Learning}, author={Meryem M'hamdi and Xiang Ren and Jonathan May}, journal={ArXiv}, year={2022}, volume={abs/2205.11152} }
The longstanding goal of multi-lingual learn- 001 ing has been to develop a universal cross- 002 lingual model that can withstand the changes 003 in multi-lingual data distributions. However, 004 most existing models assume full access to the 005 target languages in advance, whereas in realis- 006 tic scenarios this is not often the case, as new 007 languages can be incorporated later on. In this 008 paper, we present the C ross-lingual L ifelong 009 L earning (CLL) challenge, where a model is…
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References
SHOWING 1-10 OF 48 REFERENCES
On the Cross-lingual Transferability of Monolingual Representations
- Linguistics, Computer ScienceACL
- 2020
This work designs an alternative approach that transfers a monolingual model to new languages at the lexical level and shows that it is competitive with multilingual BERT on standard cross-lingUAL classification benchmarks and on a new Cross-lingual Question Answering Dataset (XQuAD).
Unsupervised Cross-lingual Representation Learning at Scale
- Computer ScienceACL
- 2020
It is shown that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks, and the possibility of multilingual modeling without sacrificing per-language performance is shown for the first time.
XNLI: Evaluating Cross-lingual Sentence Representations
- Computer ScienceEMNLP
- 2018
This work constructs an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus to 14 languages, including low-resource languages such as Swahili and Urdu and finds that XNLI represents a practical and challenging evaluation suite and that directly translating the test data yields the best performance among available baselines.
X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering
- Computer ScienceNAACL
- 2021
This work proposes X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU that adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages and shows that this approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages.
MAD-X: An Adapter-based Framework for Multi-task Cross-lingual Transfer
- Computer Science, LinguisticsEMNLP
- 2020
MAD-X is proposed, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations and introduces a novel invertible adapter architecture and a strong baseline method for adapting a pretrained multilingual model to a new language.
Transfer Learning in Natural Language Processing
- Computer ScienceNAACL
- 2019
An overview of modern transfer learning methods in NLP, how models are pre-trained, what information the representations they learn capture, and review examples and case studies on how these models can be integrated and adapted in downstream NLP tasks are presented.
MLQA: Evaluating Cross-lingual Extractive Question Answering
- Computer ScienceACL
- 2020
This work presents MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area, and evaluates state-of-the-art cross-lingual models and machine-translation-based baselines onMLQA.
LAMOL: LAnguage MOdeling for Lifelong Language Learning
- Computer ScienceICLR
- 2020
The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model.
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
- Computer ScienceEACL
- 2021
A new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains is presented, and strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection are demonstrated.
PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
- Computer ScienceEMNLP
- 2019
PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages, shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information.