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When is Multi-task Learning Beneficial for Low-Resource Noisy Code-switched User-generated Algerian Texts?

Paper i proceeding
Författare Wafia Adouane
Jean-Philippe Bernardy
Publicerad i Fourth Workshop on Computational Approaches to Linguistic Code-Switching
Förlag Language Resources and Evaluation Conference
Publiceringsår 2020
Publicerad vid Institutionen för filosofi, lingvistik och vetenskapsteori
Språk en
Ämnesord Algerian Arabic, code-switched user-generated data, multi-task learning, low-resource colloquial languages
Ämneskategorier Datorlingvistik

Sammanfattning

We investigate when is it beneficial to simultaneously learn representations for several tasks, in low-resource settings. For this, we work with noisy user-generated texts in Algerian, a low-resource non-standardised Arabic variety. That is, to mitigate the problem of the data scarcity, we experiment with jointly learning progressively 4 tasks, namely code-switch detection, named entity recognition, spell normalisation and correction, and identifying users’ sentiments. The selection of these tasks is motivated by the lack of labelled data for automatic morpho-syntactic or semantic sequence-tagging tasks for Algerian, in contrast to the case of much multi-task learning for NLP. Our empirical results show that multi-task learning is beneficial for some tasks in particular settings, and that the effect of each task on another, the order of the tasks, and the size of the training data of the task with more data do matter. Moreover, the data augmentation that we performed with no external resources has been shown to be beneficial for certain tasks.

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