Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees. We propose a new iterative predicate selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based and transition-based parsing approaches in order to handle multiple semantic head words. We train the IPS model using a combination of multi-task learning and task-specific policy gradient training. Trained this way, IPS achieves a new state of the art on the SemEval 2015 Task 18 datasets. Furthermore, we observe that policy gradient training learns an easy-first strategy.Comment: ACL2019 Long accepted. 9 pages for the paper and the additional 2 pages for the supplemental materia

Cache Transition Systems for Graph Parsing

Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition. We find empirically that small cache sizes cover a high percentage of sentences in existing semantic corpora

Neural Combinatory Constituency Parsing

東京都立大学Tokyo Metropolitan University博士（情報科学）doctoral thesi

Undirected dependency parsing

Dependency parsers, which are widely used in natural language processing tasks, employ a representation of syntax in which the structure of sentences is expressed in the form of directed links (dependencies) between their words. In this article, we introduce a new approach to transition-based dependency parsing in which the parsing algorithm does not directly construct dependencies, but rather undirected links, which are then assigned a direction in a postprocessing step. We show that this alleviates error propagation, because undirected parsers do not need to observe the single-head constraint, resulting in better accuracy. Undirected parsers can be obtained by transforming existing directed transition-based parsers as long as they satisfy certain conditions. We apply this approach to obtain undirected variants of three different parsers (the Planar, 2-Planar, and Covington algorithms) and perform experiments on several data sets from the CoNLL-X shared tasks and on the Wall Street Journal portion of the Penn Treebank, showing that our approach is successful in reducing error propagation and produces improvements in parsing accuracy in most of the cases and achieving results competitive with state-of-the-art transition-based parsers.Xunta de Galicia | Ref. CN2012/008Xunta de Galicia | Ref. CN2012/317Xunta de Galicia | Ref. CN2012/319Ministerio de Ciencia e Innovación | Ref. TIN2010-18552-C03-01Ministerio de Ciencia e Innovación | Ref. TIN2010-18552-C03-0

Cross-lingual RST Discourse Parsing

Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on cross-lingual discourse parsing.Comment: To be published in EACL 2017, 13 page