GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

Machine Learning Theory and Practice as a Source of Insight into Universal Grammar

Articl

Dependency parsing of learner English

Current syntactic annotation of large-scale learner corpora mainly resorts to “standard parsers” trained on native language data. Understanding how these parsers perform on learner data is important for downstream research and application related to learner language. This study evaluates the performance of multiple standard probabilistic parsers on learner English. Our contributions are three-fold. Firstly, we demonstrate that the common practice of constructing a gold standard – by manually correcting the pre-annotation of a single parser – can introduce bias to parser evaluation. We propose an alternative annotation method which can control for the annotation bias. Secondly, we quantify the influence of learner errors on parsing errors, and identify the learner errors that impact on parsing most. Finally, we compare the performance of the parsers on learner English and native English. Our results have useful implications on how to select a standard parser for learner English

Active learning and the Irish treebank

We report on our ongoing work in developing the Irish Dependency Treebank, describe the results of two Inter annotator Agreement (IAA) studies, demonstrate improvements in annotation consistency which have a knock-on effect on parsing accuracy, and present the final set of dependency labels. We then go on to investigate the extent to which active learning can play a role in treebank and parser development by comparing an active learning bootstrapping approach to a passive approach in which sentences are chosen at random for manual revision. We show that active learning outperforms passive learning, but when annotation effort is taken into account, it is not clear how much of an advantage the active learning approach has. Finally, we present results which suggest that adding automatic parses to the training data along with manually revised parses in an active learning setup does not greatly affect parsing accuracy

Proceedings

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 268 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

Reliability of Automatic Linguistic Annotation : Native vs Non-native Texts

Peer reviewe