Parsing enhancing of the conversational module for a service robot : Différence entre versions

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* Ivette Vélez (IIMAS/UNAM)
* Ivette Vélez (IIMAS/UNAM)
* Jorge García Flores (LIPN/UP13)
* Jorge García Flores (LIPN/UP13)
* '''May 23, 2017''': Ivette and Jorge

Version du 23 mai 2017 à 18:56


The goal of the project is to improve the syntactic parser of the Golem service robot[1][2]. 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.

Work plan


  1. Create an evaluation testbed for the parser based on (Doostdar et al., 20907)[3].
  2. Train a machine learning model to generate DGC[4]


  1. Transform the DGC-based (Definite Clause Grammar) parser into a CCG (Combinatorial Categorial Grammar)[5][6] based parser
  2. Evaluate Google's Parsey MacParseface[7] 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


  1. 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
  2. The Golem Team, RoboCup@Home 2016
  3. 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
  4. 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)
  5. 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.
  6. 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.
  7. [ Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source]