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equipes:rcln:ancien_wiki:projets:parsing_enhancing_conversationnal_service_robot [2019/04/09 12:05]
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| | ====== Parsing enhancing of the conversational module for a service robot ====== |
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| ===== Abstract ===== | ===== Abstract ===== |
| 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) [[http://link.springer.com/content/pdf/10.1007%2F978-3-642-45111-9_37.pdf|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>[[https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiN49-iv_fTAhUIqlQKHc9KBUYQFggqMAA&url=http%3A%2F%2Fwww.robocup2016.org%2Fmedia%2Fsymposium%2FTeam-Description-Papers%2FAtHome%2FRoboCup_2016_AtHome_TDP_golem.pdf&usg=AFQjCNHm5_1RldjKLeGqB-sLgXV6FdYfEw&sig2=2JWWID7uIBZFKR-Lgcgnfg|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. | The goal of the project is to improve the syntactic parser of the Golem service robot[(Meza Ruiz I.V., Rascón C., Pineda Cortes L.A. (2013) [[http://link.springer.com/content/pdf/10.1007%2F978-3-642-45111-9_37.pdf|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)][([[https://www.google.com.mx/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiN49-iv_fTAhUIqlQKHc9KBUYQFggqMAA&url=http%3A%2F%2Fwww.robocup2016.org%2Fmedia%2Fsymposium%2FTeam-Description-Papers%2FAtHome%2FRoboCup_2016_AtHome_TDP_golem.pdf&usg=AFQjCNHm5_1RldjKLeGqB-sLgXV6FdYfEw&sig2=2JWWID7uIBZFKR-Lgcgnfg|The Golem Team, RoboCup@Home 2016]])]. 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. |
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| ===== Experimental testbed for measuring the parser's performances ===== | ===== Experimental testbed for measuring the parser's performances ===== |
| ===== 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> but mostly on the Robocup command parser generator. | - Create an evaluation testbed for the parser based on (Doostdar et al., 20907)[(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)] 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>, mostly based on Claires article. | - Train a machine learning model to generate DGC[(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))], mostly based on Claires article. |
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| ==== 2018 ==== | ==== 2018 ==== |
| - 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)[(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.)][(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.)] based parser |
| - Evaluate Google's Parsey MacParseface<ref>[[https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html | - Evaluate Google's Parsey MacParseface[([[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. | Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source]])] with the service robot oriented parsing evaluation testbed. |
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| ==== 2019 ==== | ==== 2019 ==== |