Improving Domain Independent Question Parsing with Synthetic Treebanks
Automatic syntactic parsing for question constructions is a challenging task due to the paucity of training examples in most treebanks. The near absence of question constructions is due to the dominance of the news domain in treebanking efforts. In this paper, we compare two synthetic low-cost question treebank creation methods with a conventional manual high-cost annotation method in the context of three domains (news questions, political talk shows, and chatbots) for Modern Standard Arabic, a language with relatively low resources and rich morphology. Our results show that synthetic methods can be effective at significantly reducing parsing errors for a target domain without having to invest large resources on manual annotation; and the combination of manual and synthetic methods is our best domain-independent performer.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Large Discourse Treebanks from Scalable Distant Supervision
Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most recent discourse parsers (and consequentl…
Discourse ParsingSentiment AnalysisExploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning
Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain syntactic knowledge that is potential to benefit ea…
Dependency ParsingMulti-Task LearningA Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks
Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain common syntactic knowledge that is potential to ben…
Dependency ParsingInformation RetrievalMulti-Task LearningSprucing up the trees -- Error detection in treebanks
We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate…
Active LearningBayesian InferenceDomain AdaptationNamed Entity Recognition (NER)Identifying and Handling Cross-Treebank Inconsistencies in UD: A Pilot Study
The Universal Dependencies treebanks are a still-growing collection of treebanks for a wide range of languages, all annotated with a common inventory of dependency relations. Yet, the usages of the relations can be categ…