Real-life multilingual systems should be able to efficiently incorporate new languages as data distributions fed to the system evolve and shift over time. To do this, systems need to handle the issue of catastrophic forgetting, where the model performance drops for languages or tasks seen further in its past. In this paper, we study catastrophic forgetting, as well as methods to minimize this, in a massively multilingual continual learning framework involving up to 51 languages and covering both classification and sequence labeling tasks. We present LR ADJUST, a learning rate scheduling method that is simple, yet effective in preserving new information without strongly overwriting past knowledge. Furthermore, we show that this method is effective across multiple continual learning approaches. Finally, we provide further insights into the dynamics of catastrophic forgetting in this massively multilingual setup.

<iframe src="https://app.sli.do/event/aL2QM8dUtT8fzBuK3envdP/embed/polls/3dbce9a4-29c9-4c22-84bf-2170eef6e9c4" width="300" height="400"></iframe>

Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning

Named Entity Recognition is a key Natural Language Processing task whose performance is sensitive to choice of genre and language. A unified NER model across multiple genres and languages is more practical and efficient by leveraging commonalities across genres or languages. In this paper, we propose a novel setup for NER which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. We explore a range of approaches to building a unified model using domain and language adaptation techniques. Our experiments highlight multiple nuances to consider while building a unified model, including that naive data pooling fails to obtain good performance, that domain-specific adaptations are more important than language-specific ones and that including domain-specific adaptations in a unified model nears the performance of training multiple dedicated monolingual models at a fraction of their parameter count.

Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.40/)

Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders

Findings papers without presentation slot

poster

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IJCNLP-AACL 2022

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

Mayank Kulkarni

3

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SHORT BIO

Presentations

Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning

Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model

Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders

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