Humor Prevails! - Implementing a Joke Generator into a Conversational System

ML-Ask: Open Source Affect Analysis Software for Textual Input in Japanese

“…Most commonly, the system was used to analyze user input in human-agent interaction [1,3,4,15,23,24,25,26]. In particular, the analysis of user input was utilized in decision making support about which conversation strategy to choose (normal conversation or joke) [1], and in an automatic evaluation method for dialog agents [3].…”

“…The system was most often evaluated on conversations, both between humans [1] and between human users and conversational agents [4,3,15,23,26]. In [1] Dybala et al showed that ML-Ask presents comparable answers to human annotators when annotating conversations between people of different age and status (in particular young students vs. middle-aged businessmen).…”

“…In other evaluations Ptaszynski et al showed that the system performs comparably to humans when annotating human-agent dialogs. This was evaluated using only ML-Ask [3,4], and ML-Ask confronted with the Webmining procedure [23,26]. Recently Ptaszynski et al added also emoticon analysis system CAO to this evaluation [35].…”

“…• Analysis of user input [1, 3, 4, 15] [23, 24, 25, 26] [35] • Decision making support [1,4] • Automatic evaluation [3] Verification of emotion appropriateness [23,24,25,26] [35]…”

“…Depending on application, the focus could be on different states of the user, such as user engagement in conversation (e.g., with a dialog agent [1]), user intention (e.g., to buy a certain product, or chose a specific migration route [2]), user attitude (e.g., toward a specific object, or the agent itself [3]), or user emotions (e.g., to choose different conversation strategy if the user is sad or happy, etc. [4]). In many of those tasks techniques for Affect Analysis have proved to be effective.…”