5 research outputs found
A Pilot Study on Dialogue-Level Dependency Parsing for Chinese
Dialogue-level dependency parsing has received insufficient attention,
especially for Chinese. To this end, we draw on ideas from syntactic dependency
and rhetorical structure theory (RST), developing a high-quality
human-annotated corpus, which contains 850 dialogues and 199,803 dependencies.
Considering that such tasks suffer from high annotation costs, we investigate
zero-shot and few-shot scenarios. Based on an existing syntactic treebank, we
adopt a signal-based method to transform seen syntactic dependencies into
unseen ones between elementary discourse units (EDUs), where the signals are
detected by masked language modeling. Besides, we apply single-view and
multi-view data selection to access reliable pseudo-labeled instances.
Experimental results show the effectiveness of these baselines. Moreover, we
discuss several crucial points about our dataset and approach.Comment: Accepted by Findings of ACL 2023 (Camera-ready version
Multilingual discriminative lexicalized parsing
International audienceWe provide a generalization of discriminative lexicalized shift reduce parsing techniques for phrase structure grammar to a wide range of morphologically rich languages. The model is efficient and outperforms recent strong baselines on almost all languages considered. It takes advantage of a dependency based modelling of morphology and a shallow modelling of constituency boundaries
Multilingual discriminative lexicalized parsing
International audienceWe provide a generalization of discriminative lexicalized shift reduce parsing techniques for phrase structure grammar to a wide range of morphologically rich languages. The model is efficient and outperforms recent strong baselines on almost all languages considered. It takes advantage of a dependency based modelling of morphology and a shallow modelling of constituency boundaries