11 research outputs found
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from
rich-resource languages is an effective solution for named entity recognition
(NER). While all existing methods directly transfer from source-learned model
to a target language, in this paper, we propose to fine-tune the learned model
with a few similar examples given a test case, which could benefit the
prediction by leveraging the structural and semantic information conveyed in
such similar examples. To this end, we present a meta-learning algorithm to
find a good model parameter initialization that could fast adapt to the given
test case and propose to construct multiple pseudo-NER tasks for meta-training
by computing sentence similarities. To further improve the model's
generalization ability across different languages, we introduce a masking
scheme and augment the loss function with an additional maximum term during
meta-training. We conduct extensive experiments on cross-lingual named entity
recognition with minimal resources over five target languages. The results show
that our approach significantly outperforms existing state-of-the-art methods
across the board.Comment: This paper is accepted by AAAI2020. Code is available at
https://github.com/microsoft/vert-papers/tree/master/papers/Meta-Cros
Meta-learning for fast cross-lingual adaptation in dependency parsing
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