Unsupervised Dependency Parsing: Let's Use Supervised Parsers

We present a self-training approach to unsupervised dependency parsing that reuses existing supervised and unsupervised parsing algorithms. Our approach, called `iterated reranking' (IR), starts with dependency trees generated by an unsupervised parser, and iteratively improves these trees using the richer probability models used in supervised parsing that are in turn trained on these trees. Our system achieves 1.8% accuracy higher than the state-of-the-part parser of Spitkovsky et al. (2013) on the WSJ corpus.Comment: 11 page

Deep Multitask Learning for Semantic Dependency Parsing

We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two multitask learning approaches---one that shares parameters across formalisms, and one that uses higher-order structures to predict the graphs jointly. We find that both approaches improve performance across formalisms on average, achieving a new state of the art. Our code is open-source and available at https://github.com/Noahs-ARK/NeurboParser.Comment: Proceedings of ACL 201

Towards an implementable dependency grammar

The aim of this paper is to define a dependency grammar framework which is both linguistically motivated and computationally parsable. See the demo at http://www.conexor.fi/analysers.html#testingComment: 10 page

Incremental Interpretation: Applications, Theory, and Relationship to Dynamic Semantics

Why should computers interpret language incrementally? In recent years psycholinguistic evidence for incremental interpretation has become more and more compelling, suggesting that humans perform semantic interpretation before constituent boundaries, possibly word by word. However, possible computational applications have received less attention. In this paper we consider various potential applications, in particular graphical interaction and dialogue. We then review the theoretical and computational tools available for mapping from fragments of sentences to fully scoped semantic representations. Finally, we tease apart the relationship between dynamic semantics and incremental interpretation.Comment: Procs. of COLING 94, LaTeX (2.09 preferred), 8 page

Description of the CUDF Format

This document contains several related specifications, together they describe the document formats related to the solver competition which will be organized by Mancoosi. In particular, this document describes: - DUDF (Distribution Upgradeability Description Format), the document format to be used to submit upgrade problem instances from user machines to a (distribution-specific) database of upgrade problems; - CUDF (Common Upgradeability Description Format), the document format used to encode upgrade problems, abstracting over distribution-specific details. Solvers taking part in the competition will be fed with input in CUDF format