Cross-lingual and cross-domain discourse segmentation of entire documents
TL
TL;DR
This paper proposes statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations, and considers the problem of learning discourse segmenter when no labeled data is available for a language.
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.
References47 items
1
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments
2
UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing
3
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss
4
Improving sentence compression by learning to predict gaze
5