Parsing enhancing of the conversational module for a service robot
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Révision de 31 mai 2017 à 18:15 par Jgflores (discussion | contributions) (→Machine learining model selection)
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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
- Generate 100 sentences with the Robocup command generator and evaluate the accuracy of the generated command manually.
- 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
- Create an evaluation testbed for the parser based on (Doostdar et al., 20907)[4].
- Train a machine learning model to generate DGC[5]
2018
- Transform the DGC-based (Definite Clause Grammar) parser into a CCG (Combinatorial Categorial Grammar)[6][7] based parser
- Evaluate Google's Parsey MacParseface[8] with the service robot oriented parsing evaluation testbed.
2019
- 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
- ↑ 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
- ↑ The Golem Team, RoboCup@Home 2016
- ↑ 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).
- ↑ 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
- ↑ 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)
- ↑ 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.
- ↑ 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.
- ↑ [https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source]