# Cross-lingual and cross-domain discourse segmentation of entire documents

## TL;DR

This paper introduces statistical discourse segmenters for multiple languages and domains that do not depend on gold annotations, achieving high accuracy and addressing the challenge of unlabeled data in discourse segmentation.

## Contribution

It presents the first multilingual, cross-domain discourse segmenters that operate without gold pre-annotations and explores learning with no labeled data.

## Key findings

- Achieved 89.5% F1 on English newswire
- Demonstrated effective cross-lingual segmentation
- Performed well across multiple domains

## Abstract

Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when no labeled data is available for a language. Our fully supervised system obtains 89.5% F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total.