EusDisParser: improving an under-resourced discourse parser with cross-lingual data
Development of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since the corpus is very small. In this paper, we create the first demonstrator based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser. More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language. We found that our approach to building a system limited to the small set of data available for Basque allowed us to get an improvement over previous approaches making use of many data annotated in other languages. At best, we get 34.78 in F1 for the full discourse structure. More data annotation is necessary in order to improve the results obtained with these techniques. We also describe which relations match with the gold standard, in order to understand these results.
Code (0)
등록된 구현이 없습니다.
Tasks
Multilingual Word EmbeddingsWord EmbeddingsSimilar Papers 제목 키워드 기반
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations
Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and …
Semantic ParsingPersian Rhetorical Structure Theory
Over the past years, interest in discourse analysis and discourse parsing has steadily grown, and many discourse-annotated corpora and, as a result, discourse parsers have been built. In this paper, we present a discours…
Discourse ParsingTraining Data Enrichment for Infrequent Discourse Relations
Discourse parsing is a popular technique widely used in text understanding, sentiment analysis and other NLP tasks. However, for most discourse parsers, the performance varies significantly across different discourse rel…
Discourse ParsingRelationSentiment AnalysisBeyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
Rewriting inputs to improve frozen downstream models has become a common strategy in modern NLP pipelines. Prior work on incremental dialogue discourse parsing (DDP) shows that supervised clarification models can rewrite…
Discourse ParsingA Dependency Perspective on RST Discourse Parsing and Evaluation
Computational text-level discourse analysis mostly happens within Rhetorical Structure Theory (RST), whose structures have classically been presented as constituency trees, and relies on data from the RST Discourse Treeb…
Constituency ParsingDependency ParsingDiscourse Parsing