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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.
Contextualized word representations have proven useful for various natural language processing tasks. However, it remains unclear to what extent these representations can cover hand-coded semantic information such as semantic frames, which specify th
Existing text style transfer (TST) methods rely on style classifiers to disentangle the texts content and style attributes for text style transfer. While the style classifier plays a critical role in existing TST methods, there is no known investigat
Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a smal
We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents. We apply our method to reading comprehension tasks by encoding questions and pa
Syntactic information contains structures and rules about how text sentences are arranged. Incorporating syntax into text modeling methods can potentially benefit both representation learning and generation. Variational autoencoders (VAEs) are deep g