Meta-learning for fast cross-lingual adaptation in dependency parsing

• URL: http://arxiv.org/abs/2104.04736v2
• Date: Tue, 13 Apr 2021 16:10:40 GMT
• Title: Meta-learning for fast cross-lingual adaptation in dependency parsing
• Authors: Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
• Abstract summary: We apply model-agnostic meta-learning to the task of cross-lingual dependency parsing. We find that meta-learning with pre-training can significantly improve upon the performance of language transfer.
• Score: 16.716440467483096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of cross-lingual dependency parsing. We train our model on a diverse set of languages to learn a parameter initialization that can adapt quickly to new languages. We find that meta-learning with pre-training can significantly improve upon the performance of language transfer and standard supervised learning baselines for a variety of unseen, typologically diverse, and low-resource languages, in a few-shot learning setup.

Related papers

• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• On the cross-lingual transferability of multilingual prototypical models across NLU tasks [2.44288434255221]
  Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven to be effective for limited domain and language applications. In practice, these approaches suffer from the drawbacks of domain-driven design and under-resourced languages. This article proposes to investigate the cross-lingual transferability of using synergistically few-shot learning with prototypical neural networks and multilingual Transformers-based models.
  arXiv  Detail & Related papers  (2022-07-19T09:55:04Z)
• Adaptive Activation Network For Low Resource Multilingual Speech Recognition [30.460501537763736]
  We introduce an adaptive activation network to the upper layers of ASR model. We also proposed two approaches to train the model: (1) cross-lingual learning, replacing the activation function from source language to target language, and (2) multilingual learning. Our experiments on IARPA Babel datasets demonstrated that our approaches outperform the from-scratch training and traditional bottleneck feature based methods.
  arXiv  Detail & Related papers  (2022-05-28T04:02:59Z)
• Cross-lingual Lifelong Learning [53.06904052325966]
  We present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm. We provide insights into what makes multilingual sequential learning particularly challenging. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata.
  arXiv  Detail & Related papers  (2022-05-23T09:25:43Z)
• X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering [55.57776147848929]
  We propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for Natural Language Understanding (NLU) Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages.
  arXiv  Detail & Related papers  (2021-04-20T00:13:35Z)
• MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning [91.5426763812547]
  Cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. We propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one.
  arXiv  Detail & Related papers  (2021-04-16T06:15:52Z)
• Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling [12.497781134446898]
  We address the data scarcity and limitations of linguistic theory by proposing language-agnostic multi-task training methods. First, we introduce a meta-learning-based approach, meta-transfer learning, in which information is judiciously extracted from high-resource monolingual speech data to the code-switching domain. Second, we propose a novel multilingual meta-ems approach to effectively represent code-switching data by acquiring useful knowledge learned in other languages. Third, we introduce multi-task learning to integrate syntactic information as a transfer learning strategy to a language model and learn where to code-switch.
  arXiv  Detail & Related papers  (2021-04-13T14:49:26Z)
• Multilingual and cross-lingual document classification: A meta-learning approach [24.66829920826166]
  We propose a meta-learning approach to document classification in limited-resource setting. We show effectiveness in two settings: few-shot, cross-lingual adaptation to previously unseen languages and multilingual joint training.
  arXiv  Detail & Related papers  (2021-01-27T10:22:56Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)
• Zero-Shot Cross-Lingual Transfer with Meta Learning [45.29398184889296]
  We consider the setting of training models on multiple languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks.
  arXiv  Detail & Related papers  (2020-03-05T16:07:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.