@inproceedings{mishra-haghighi-2021-improved,
    title = "Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair Prediction",
    author = "Mishra, Shubhanshu  and
      Haghighi, Aria",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2021.wnut-1.42/",
    doi = "10.18653/v1/2021.wnut-1.42",
    pages = "381--388",
    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: \url{https://github.com/twitter-research/multilingual-alignment-tpp}."
}