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arXiv · 2009.11152

Hierarchical Pre-training for Sequence Labelling in Spoken Dialog

Emile Chapuis·Pierre Colombo·Matteo Manica·Matthieu Labeau·Chloe Clavel
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Abstract

Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (\texttt{SILICONE}). \texttt{SILICONE} is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over $2.3$ billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.

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Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, Chloe Clavel. 2020-09-23. Hierarchical Pre-training for Sequence Labelling in Spoken Dialog. https://arxiv.org/abs/2009.11152

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