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

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==Work plan==
 
==Work plan==
 
===2017===
 
===2017===
# Develop an evaluation testbed for the parser based on <ref>[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]</ref>
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# Develop 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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# 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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===2018===
 
===2018===
 
===2018===
 
===2018===

Version du 17 mai 2017 à 17:39

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

2018

2018

  1. Create an evaluation testbed for the

Participants

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)