[Paper Review] Clustering Contextualized Representations of Text for Unsupervised Syntax Induction.

Vikram Gupta, Haoyue Shi+2 more|arXiv (Cornell University)|Oct 24, 2020

Topic Modeling33 references4 citations

TL;DR

This paper proposes a deep embedded clustering approach that jointly learns low-dimensional, cluster-friendly representations and performs clustering on contextualized text embeddings for unsupervised syntax induction. It achieves strong performance on part-of-speech induction (12-tag, 10 languages) and competitive results on constituency labeling, establishing new baselines using contextualized representations.

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.

Motivation & Objective

• To address the challenge of unsupervised syntax induction without annotated training data.
• To improve part-of-speech induction (POSI) and constituency labeling (CoLab) using contextualized text representations.
• To develop a joint representation learning and clustering framework that enhances clustering performance.
• To evaluate the effectiveness of multilingual contextualized representations for syntax induction tasks.
• To establish strong baselines for unsupervised syntax induction using modern contextualized embeddings.

Proposed method

• Proposes a deep embedded clustering framework that jointly optimizes representation learning and clustering in a shared latent space.
• Transforms contextualized text representations into a lower-dimensional space that is more amenable to clustering.
• Enhances representations with task-specific components to improve clustering quality.
• Utilizes multilingual contextualized representations to explore cross-lingual transfer for syntax induction.
• Employs a clustering objective that is end-to-end differentiable and jointly optimized with the representation learning process.
• Applies contrastive learning or similar regularization to improve cluster separation in the latent space.

Experimental results

Research questions

• RQ1 Can deep embedded clustering effectively induce syntactic structures from contextualized text representations in an unsupervised manner?
• RQ2 How do task-specific representations improve clustering performance for syntax induction?
• RQ3 To what extent do multilingual contextualized representations generalize across languages in unsupervised syntax induction?
• RQ4 What is the performance of the proposed method on 12-tag and 45-tag POSI benchmarks across multiple languages?
• RQ5 How does the method compare to prior unsupervised syntax induction approaches in terms of accuracy and robustness?

Key findings

• The method achieves state-of-the-art performance on 12-tag part-of-speech induction across 10 languages using contextualized representations.
• It establishes competitive results on 45-tag part-of-speech induction, setting a new strong baseline for this task.
• Constitutional labeling (CoLab) performance is competitive, demonstrating the method's generalization to syntactic structure induction.
• The integration of task-specific representations leads to measurable improvements in clustering quality.
• Multilingual representations show effectiveness in cross-lingual syntax induction, supporting transfer learning across languages.
• The joint optimization of representation learning and clustering yields more coherent and accurate syntactic clusters than separate approaches.