Discovering Elementary Discourse Units in Textual Data Using Canonical Correlation Analysis
This provides a simple, language-independent baseline for text segmentation, particularly useful when labeled data is scarce.
The paper tackled the problem of unsupervised segmentation of text into Elementary Discourse Units (EDUs) using Canonical Correlation Analysis, achieving competitive results on Semantic Textual Similarity and Mohler datasets, even outperforming some supervised methods.
Canonical Correlation Analysis (CCA) has been exploited immensely for learning latent representations in various fields. This study takes a step further by demonstrating the potential of CCA in identifying Elementary Discourse Units(EDUs) that captures the latent information within the textual data. The probabilistic interpretation of CCA discussed in this study utilizes the two-view nature of textual data, i.e. the consecutive sentences in a document or turns in a dyadic conversation, and has a strong theoretical foundation. Furthermore, this study proposes a model for Elementary Discourse Unit(EDU) segmentation that discovers EDUs in textual data without any supervision. To validate the model, the EDUs are utilized as textual unit for content selection in textual similarity task. Empirical results on Semantic Textual Similarity(STSB) and Mohler datasets confirm that, despite represented as a unigram, the EDUs deliver competitive results and can even beat various sophisticated supervised techniques. The model is simple, linear, adaptable and language independent making it an ideal baseline particularly when labeled training data is scarce or nonexistent.