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

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===2018===
 
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# Transform the DGC-based (Definite Clause Grammar) parser into a CCG (Combinatorial Categorial Grammar)<ref>R. Cantrell, M. Scheutz, P. Schermerhorn and X. Wu, "[https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiDm9Shu_fTAhXEs1QKHfAQD4AQFggzMAE&url=https%3A%2F%2Fpdfs.semanticscholar.org%2F428f%2Fcad8ea088d14cc292d2e17d151dd7b7a088e.pdf&usg=AFQjCNHXZSrCbItkGZ37utw6wvkG6I2DEQ&sig2=293KRGnAJj8D1pXwbztdEg 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, "[https://arxiv.org/pdf/1604.06721 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
 
# Transform the DGC-based (Definite Clause Grammar) parser into a CCG (Combinatorial Categorial Grammar)<ref>R. Cantrell, M. Scheutz, P. Schermerhorn and X. Wu, "[https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiDm9Shu_fTAhXEs1QKHfAQD4AQFggzMAE&url=https%3A%2F%2Fpdfs.semanticscholar.org%2F428f%2Fcad8ea088d14cc292d2e17d151dd7b7a088e.pdf&usg=AFQjCNHXZSrCbItkGZ37utw6wvkG6I2DEQ&sig2=293KRGnAJj8D1pXwbztdEg 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, "[https://arxiv.org/pdf/1604.06721 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
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# Plan the integration of Google's Parsey MacParseface<ref>[https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html
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Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source]
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===2019===
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# Integrate Parsey MacParceface
  
 
===2018===
 
===2018===

Version du 17 mai 2017 à 17:53

Abstract

The goal of the project is to improve the syntactic parser of the Golem robot. 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

2017

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

2018

  1. Transform the DGC-based (Definite Clause Grammar) parser into a CCG (Combinatorial Categorial Grammar)[3][4] based parser
  2. Plan the integration of Google's Parsey MacParseface[5]
    1. 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
    2. 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)
    3. 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.
    4. 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.
    5. [https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source]

      2019

      1. Integrate Parsey MacParceface

      2018

      1. Create an evaluation testbed for the

      Participants

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

      Références

      1. 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
      2. 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)
      3. 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.
      4. 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.
      5. [https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source]

        2019

        1. Integrate Parsey MacParceface

        2018

        1. Create an evaluation testbed for the

        Participants

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

        Références

        <references/>