Abstract
This paper presents a novel framework for constructing a Semantic-Based Conversational Agent (SCAF). Traditional conversational agents (CA) interpret scripts using structural patterns of sentences, which require the script writer to consider every possible permutation that a user may send as input to the CA. This is a time-consuming process, which takes no consideration of semantic content, working solely with the structural form of the sentence. Furthermore, this has proven to be a high maintenance task that can produce some unforeseen consequences when modifying or introducing new patterns into a script. This invariably results in the script writer reassessing the entire script to prevent such occurrences. Different script writers possess differing levels of skill and as such this can prove to be an exasperating task. The proposed SCAF interprets scripts consisting of natural language sentences by means of a semantic sentence similarity measure. User input is measured semantically against the natural language sentences of the context in order to respond with an appropriate output. Such scripting is effortless and alleviates the burden of the traditional pattern-scripted methodologies. Evaluation of the framework has highlighted its potential and shown improvements on traditional CAs.
Similar content being viewed by others
References
Michie D (2001) Return of the imitation game. Electron Trans Artif Intell 6(2):203–221
Massaro DW, Cohen MM, Beskow WJ, Cole RA (1998) Developing and evaluating conversational agents. University of California, Santa Cruz
Cassell J (2000) Embodied conversational agents. MIT Press, Cambridge
Sanders GA, Scholtz J (2000) Measurement and evaluation of embodied conversational agents. In: Cassell J, Sullivan J, Prevost S, Churchill E (eds) Embodied conversational agents. MIT Press, Cambridge
Isbister K, Doyle P (2004) The blind men and the elephant revisited: evaluating interdisciplinary embodied conversational agents. In: From brows to trust, Ruttkay Z, Pelachaud C (eds) Kluwer Academic, Netherlands, pp 3–26, Chap 1
Malatesta L, Raouzalou A, Karpouzis K, Kollias K (2009) Towards modeling embodied conversational agent character profiles using appraisal theory predictions in expression synthesis. Int J Appl Intell 30(1):58–64
Weizenbaum J (1966) ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM 9:36–45
Colby K (1975) Artificial paranoia: a computer simulation of paranoid process. Pergamon Press, New York. Cited by Mauldin ML (1994). Chatterbots, tinymuds, and the turing test: entering the Loebner prize competition. Carnegie Mellon University, Pittsburgh
Carpenter R (2006) Jabberwacky. http://jabberwacky.com. Accessed 14.04.11
Chatbots (2009) http://chatbots.org/chatbot/freudbot. Accessed 13.03.11
ejTalk (2009) http://ejTalk.com. Accessed 13.03.11
Chatbots (2010) http://chatbots.org/chatbot/suzette. Accessed 13.03.11
Wallace R (2003) The elements of AIML style. ALICE Artificial Intelligence Foundations, Inc. Cited by Schmaker RP, Chen H (2008). Interaction analysis of the ALICE chatterbot: a two-study investigation of dialog and domain questioning. University of Arizona
Convagent (2001) InfoBot scripter’s manual. www.convagent.com. Accessed 14.03.11
Landauer TK, Foltz PW, Laham D (1998) Introduction to latent semantic analysis. Discourse Process 25(2–3):259–284
Li Y, McLean D, Bandar Z, O’Shea JD, Crockett K (2006) Sentence similarity based on semantic nets and corpus statistics. IEEE Trans Knowl Data Eng 18(8):1138–1149
Sammut C (2001) Managing context in a conversational agent. Electron Trans Artif Intell, Sect B, 5, 189–202. http://www.ep.liu.se/ej/etai/2001/011/
Li Y, Bandar ZA, McLean D (2003) An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans Knowl Data Eng 15(4):871–881
Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11), 39–41
Corpus Brown Information (2005) http://www.clwww.essex.ac.uk/w3s/corpus_ling/content/corpora/list/private/brown/brown.htm. Accessed 01.04.11
Walker A, Litman DJ, Kamm CA, Abella A (1997) PARADISE: a framework for evaluating spoken dialogue agents. In: Proc of the 35th annual meeting of the assoc for computational linguistics. Morgan Kaufmann, San Francisco. Cited by Hjalmarsson A (2002). Evaluating adapt, a multi-modal conversational, dialogue system using PARADISE. Master’s thesis, Royal Institute of Technology, Stockholm
Ruttkay Z, Dormann C, Noot H (2004) Embodied conversational agents on a common ground: a framework for design and evaluation. In: From brows to trust, Ruttkay Z, Pelachaud C (eds) Kluwer Academic, Norwell, pp 27–66, Chap 2
Neale JM, Liebert RM (1986) Science and behaviour an introduction to methods of research. Prentice Hall, New York. Cited by Christoph N (2004) Empirical evaluation methodology for embodied conversational agents: on conducting evaluation studies. Chap 3. In: From brows to trust, Ruttkay Z, Pelachaud C (eds) Kluwer Academic, pp 67–99, 2004
Faul F, Erdfelder E, Lang A-G, Buchner A (2007) G* Power: a flexible statistical power analysis program for the social sciences. Behav Res Methods 39(2):175–191
On B-W, Lee I (2011) Meta similarity. Int J Appl Intell 35(3):359–374
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
O’Shea, K. An approach to conversational agent design using semantic sentence similarity. Appl Intell 37, 558–568 (2012). https://doi.org/10.1007/s10489-012-0349-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-012-0349-9