AuthorsBenjamin Muller, Deepanshu, Gupta Siddharth, Patwardhan, Jean-Philippe Fauconnier, David Vandyke, Sachin Agarwal
Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lingual transfer from high-resource ones. In this work, we try to better understand how such models, specifically mT5, transfer any linguistic and semantic knowledge across languages, even though no explicit cross-lingual signals are provided during pre-training. Rather, only unannotated texts from each language are presented to the model separately and independently of one another, and the model appears to implicitly learn cross-lingual connections. This raises several questions that motivate our study, such as: Are the cross-lingual connections between every language pair equally strong? What properties of source and target language impact the strength of cross-lingual transfer? Can we quantify the impact of those properties on the cross-lingual transfer?
In our investigation, we analyze a pre-trained mT5 to discover the attributes of cross-lingual connections learned by the model. Through a statistical interpretation framework over 90 language pairs across three tasks, we show that transfer performance can be modeled by a few linguistic and data-derived features. These observations enable us to interpret cross-lingual understanding of the mT5 model. Based on this, one can favorably choose the best source language for a task, and can anticipate its training data demands. A key finding of this work is that similarity of syntax, morphology and phonology are good predictors of cross-lingual transfer, significantly more than just the lexical similarity of languages. For a given language, we are able to predict zero-shot performance, that increases on a logarithmic scale with the number of few-shot target language data points.
Users expect Siri speech recognition to work well regardless of language, device, acoustic environment, or communication channel bandwidth. Like many other supervised machine learning tasks, achieving such high accuracy usually requires large amounts of labeled data. Whenever we launch Siri in a new language, or extend support to different audio channel bandwidths, we face the challenge of having enough data to train our acoustic models. In this article, we discuss transfer learning techniques that leverage data from acoustic models already in production. We show that the representations are transferable not only across languages but also across audio channel bandwidths. As a case study, we focus on recognizing narrowband audio over 8 kHz Bluetooth headsets in new Siri languages. Our techniques help to improve significantly Siri’s accuracy on the day we introduce a new language.
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