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

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(Dynamic command generation test)
(2017)
 
Ligne 17 : Ligne 17 :
 
==Work plan==
 
==Work plan==
 
===2017===
 
===2017===
# Create an evaluation testbed for the parser based on (Doostdar et al., 20907)<ref>Doostdar M., Schiffer S., Lakemeyer G. (2009) [https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjUxPj4uPfTAhXnrlQKHRINDx4QFgg2MAE&url=https%3A%2F%2Fwww.kbsg.rwth-aachen.de%2Fsites%2Fkbsg%2Ffiles%2FRoiSpeR_camera.pdf&usg=AFQjCNHchFN--JqxyGPNAb1PxvlxsHB5vg&sig2=hZL2uE4Bq06PoB02zYxz0w 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>.  
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# Create an evaluation testbed for the parser based on (Doostdar et al., 20907)<ref>Doostdar M., Schiffer S., Lakemeyer G. (2009) [https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjUxPj4uPfTAhXnrlQKHRINDx4QFgg2MAE&url=https%3A%2F%2Fwww.kbsg.rwth-aachen.de%2Fsites%2Fkbsg%2Ffiles%2FRoiSpeR_camera.pdf&usg=AFQjCNHchFN--JqxyGPNAb1PxvlxsHB5vg&sig2=hZL2uE4Bq06PoB02zYxz0w 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.  
# Train a machine learning model to generate DGC<ref>Tarau, P. and Figa, E.: [https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwip84ztuffTAhVmiFQKHfaYBWkQFgg5MAI&url=http%3A%2F%2Fwww.cs.bham.ac.uk%2F~lxz%2Fedrama%2Ftarau.pdf&usg=AFQjCNEk4iOhu1rsfdCUvHYOxyb_3eGkOg&sig2=Qz16T-CCJojtn6AP7ewFIg 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>
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# Train a machine learning model to generate DGC<ref>Tarau, P. and Figa, E.: [https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwip84ztuffTAhVmiFQKHfaYBWkQFgg5MAI&url=http%3A%2F%2Fwww.cs.bham.ac.uk%2F~lxz%2Fedrama%2Ftarau.pdf&usg=AFQjCNEk4iOhu1rsfdCUvHYOxyb_3eGkOg&sig2=Qz16T-CCJojtn6AP7ewFIg 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.
  
 
===2018===
 
===2018===

Version actuelle en date du 31 mai 2017 à 18:18

Abstract

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.

Experimental testbed for measuring the parser's performances

Dynamic command generation test

  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.

Direct Clause Grammar Induction with machine learning

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.

Machine learining model selection

We will implement the deep learning model used in [3] in order to generate speech act Prolog rules for Golem.

Work plan

2017

  1. Create an evaluation testbed for the parser based on (Doostdar et al., 20907)[4] but mostly on the Robocup command parser generator.
  2. Train a machine learning model to generate DGC[5], mostly based on Claires article.

2018

  1. Transform the DGC-based (Definite Clause Grammar) parser into a CCG (Combinatorial Categorial Grammar)[6][7] based parser
  2. Evaluate Google's Parsey MacParseface[8] with the service robot oriented parsing evaluation testbed.

2019

  1. Integrate Parsey MacParceface

Team

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

Meetings

  • May 23, 2017: Ivette and Jorge

References

  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. 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).
  4. 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
  5. 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)
  6. 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.
  7. 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.
  8. [https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source]