Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair Prediction

• URL: http://arxiv.org/abs/2110.10318v1
• Date: Wed, 20 Oct 2021 00:06:26 GMT
• Title: Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair Prediction
• Authors: Shubhanshu Mishra, Aria Haghighi
• Abstract summary: We evaluate a simple approach to improving zero-shot multilingual transfer of mBERT on social media corpus. Our approach assumes access to translations between source-target language pairs. We show improvements from TPP pretraining over mBERT alone in zero-shot transfer from English to Hindi, Arabic, and Japanese.
• Score: 1.14219428942199
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We evaluate a simple approach to improving zero-shot multilingual transfer of mBERT on social media corpus by adding a pretraining task called translation pair prediction (TPP), which predicts whether a pair of cross-lingual texts are a valid translation. Our approach assumes access to translations (exact or approximate) between source-target language pairs, where we fine-tune a model on source language task data and evaluate the model in the target language. In particular, we focus on language pairs where transfer learning is difficult for mBERT: those where source and target languages are different in script, vocabulary, and linguistic typology. We show improvements from TPP pretraining over mBERT alone in zero-shot transfer from English to Hindi, Arabic, and Japanese on two social media tasks: NER (a 37% average relative improvement in F1 across target languages) and sentiment classification (12% relative improvement in F1) on social media text, while also benchmarking on a non-social media task of Universal Dependency POS tagging (6.7% relative improvement in accuracy). Our results are promising given the lack of social media bitext corpus. Our code can be found at: https://github.com/twitter-research/multilingual-alignment-tpp.

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