The goal of the project is to improve the syntactic parser of the Golem service robot<ref>Meza Ruiz I.V., Rascón C., Pineda Cortes L.A. (2013) Practical Speech Recognition for Contextualized Service Robots. In: Castro F., Gelbukh A., González M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science, vol 8266. Springer, Berlin, Heidelberg</ref><ref>The Golem Team, RoboCup@Home 2016</ref>. The parsing module takes its imput from the Automatic Speech Recognition (ASR), which produces a text line to be parsed. The output of the parser is a SitLog command which triggers an action in the robot.

1. Generate 100 sentences with the Robocup command generator and evaluate the accuracy of the generated command manually.
2. TODO
   • Gather 1000 significant current linguistic expressions and corresponding SitLog commands from Golem for the Gold Standart test
   • Implement the random command generator for the Dynamic test
   • Implement both test.

The goal of this module is to train a model with linguistic and Sitlog commands in order to generate Golem's speech act rules in prolog.

We will implement the deep learning model used in <ref>Amin Sleimi and Claire Gardent. Generating Paraphrases from DBPedia using Deep Learning. WebNLG 2016, The Second International Workshop on NLG and the Semantic Web, September 6 2016, Edinburgh (Scotland).</ref> in order to generate speech act Prolog rules for Golem.

1. Create an evaluation testbed for the parser based on (Doostdar et al., 20907)<ref>Doostdar M., Schiffer S., Lakemeyer G. (2009) A Robust Speech Regognition System for Service-Robots Application. In: Iocchi L., Matsubara H., Weitzenfeld A., Zhou C. (eds) RoboCup 2008: Robot Soccer World Cup XII. RoboCup 2008. Lecture Notes in Computer Science, vol 5399. Springer, Berlin, Heidelberg</ref> but mostly on the Robocup command parser generator.
2. Train a machine learning model to generate DGC<ref>Tarau, P. and Figa, E.: Knowledge Based Conversational Agents and Virtual Storytelling. In Proceedings of the 2004 ACM Symposium on Applied Computing (Nicosia, Cyprus, March 14 - 17, 004). SAC '04. ACM Press, New York, NY, 39-44. (2004)</ref>, mostly based on Claires article.

1. Transform the DGC-based (Definite Clause Grammar) parser into a CCG (Combinatorial Categorial Grammar)<ref>R. Cantrell, M. Scheutz, P. Schermerhorn and X. Wu, “Robust spoken instruction understanding for HRI,” 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Osaka, 2010, pp. 275-282.</ref><ref>M. Eppe, S. Trott and J. Feldman, “Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, 2016, pp. 731-738.</ref> based parser
2. Evaluate Google's Parsey MacParseface<ref>https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source</ref> with the service robot oriented parsing evaluation testbed.

1. Integrate Parsey MacParceface

• Luis Pineda (IIMAS/UNAM)
• Ivette Vélez (IIMAS/UNAM)
• Jorge García Flores (LIPN/UP13)

• May 23, 2017: Ivette and Jorge