A New Corpus for Context-Dependent Semantic Parsing

Semantic parsing is the task of translating natural language (NL) utterances into a machineinterpretable meaning representation (MR). Most approaches to this task have been developed and evaluated on a small number of existing corpora. While these corpora have made progress in semantic parsing possible, most of them cover rather narrow domains and context is rarely considered. In this paper we present a new set of guidelines for context-dependent semantic parsing and describe the annotation of a semantic parsing corpus. This new corpus covers a wider domain, namely tourismrelated activities in a city, and consists of 17 dialogs containing 2374 user utterances.

[1]  J. F. Kelley,et al.  An empirical methodology for writing user-friendly natural language computer applications , 1983, CHI '83.

[2]  Tomek Strzalkowski,et al.  From Discourse to Logic , 1991 .

[3]  Uwe Reyle,et al.  From Discourse to Logic - Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory , 1993, Studies in linguistics and philosophy.

[4]  Alexander I. Rudnicky,et al.  Expanding the Scope of the ATIS Task: The ATIS-3 Corpus , 1994, HLT.

[5]  Jean Carletta,et al.  Assessing Agreement on Classification Tasks: The Kappa Statistic , 1996, CL.

[6]  John M. Zelle,et al.  Using inductive logic programming to automate the construction of natural language parsers , 1996 .

[7]  Richard M. Schwartz,et al.  A Fully Statistical Approach to Natural Language Interfaces , 1996, ACL.

[8]  Dan Flickinger,et al.  Minimal Recursion Semantics: An Introduction , 2005 .

[9]  Raymond J. Mooney,et al.  Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing , 2001, ECML.

[10]  Gregory Kuhlmann and Peter Stone and Raymond J. Mooney and Shavlik.  Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer , 2004, AAAI 2004.

[11]  Julia Hirschberg,et al.  V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.

[12]  Luke S. Zettlemoyer,et al.  Learning Context-Dependent Mappings from Sentences to Logical Form , 2009, ACL.

[13]  Milica Gasic,et al.  Spoken language understanding from unaligned data using discriminative classification models , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  James Bailey,et al.  Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance , 2010, J. Mach. Learn. Res..

[15]  Gökhan Tür,et al.  What is left to be understood in ATIS? , 2010, 2010 IEEE Spoken Language Technology Workshop.

[16]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[17]  Mark Steedman,et al.  Lexical Generalization in CCG Grammar Induction for Semantic Parsing , 2011, EMNLP.

[18]  Dan Klein,et al.  Learning Dependency-Based Compositional Semantics , 2011, CL.

[19]  Oliver Lemon,et al.  Evaluating a City Exploration Dialogue System with Integrated Question-Answering and Pedestrian Navigation , 2013, ACL.

[20]  Jacob Andreas,et al.  Semantic Parsing as Machine Translation , 2013, ACL.

[21]  Bonnie Webber.  A Formal Approach to Discourse Anaphora , 2016 .