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Clustering Contextualized Representations of Text for Unsupervised Syntax Induction

2020-10-24 05:06:29
Vikram Gupta, Haoyue Shi, Kevin Gimpel, Mrinmaya Sachan
arXiv_CL
       
Abstract

We explore clustering of contextualized text representations for two unsupervised syntax induction tasks: part of speech induction (POSI) and constituency labelling (CoLab). We propose a deep embedded clustering approach which jointly transforms these representations into a lower dimension cluster friendly space and clusters them. We further enhance these representations by augmenting them with task-specific representations. We also explore the effectiveness of multilingual representations for different tasks and languages. With this work, we establish the first strong baselines for unsupervised syntax induction using contextualized text representations. We report competitive performance on 45-tag POSI, state-of-the-art performance on 12-tag POSI across 10 languages, and competitive results on CoLab.

Abstract (translated)

URL

https://arxiv.org/abs/2010.12784

PDF

https://arxiv.org/pdf/2010.12784.pdf

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