Dialog Processing ADDING NONVERBAL BEHAVIOUR

Multimodal-Eliza Perceives and Responds to Emotion – S. Fitrianie L.J.M Rothkrantz ISSN 1858-1633 2005 ICTS 155 possibilities of reply sentences based on their topic and history than Eliza. Our developed QA system uses Wallace’s pattern matching operation.

3. ADDING NONVERBAL BEHAVIOUR

Our developed system is capable to extract emotion indications or emotion eliciting factors from a dialog. The system will reason the results to trigger one of possible displayed expressions. As a reference, we have performed an experiment to determine a list of possible expressions applied by our QA system.

3.1. Dialog Processing

Our prototype extracts emotion-eliciting factors from a dialog using two approaches. First, the system analyzes the choices of words in a string. For this purpose, we developed an emotive lexicon dictionary. Currently, it consists of 347 emotion words merged from 000. Based on 0, the words were depicted into eight octants of valence-arousal see table 1. For some ambiguous emotion words, we used a thesaurus to figure out the closeness semantic meaning of the words with other words within an octant. A parser matches the string against the dictionary and calculates a counter C for “pleasant” and “unpleasant” using the following equation: ∀ l i ∈ d i | C i t = C i t-1 + I i . s ∀ j ≠ i| C j t = C j t-1 – I i 3 Where, l is the lexicon and d is the dictionary, i is the active pleasantness, I is the lexicon’s arousal degree, s is a summation factor, and j is [pleasant, unpleasant]. The system will take the counter with the highest values. category affect name=”neutral” patternWHAT IS YOUR NAMEpattern thatthat template setconcernpleasantsetconcern setaffectpleasantsetaffectMy set_topicnameset is bot name=”name”. template affectcategory topic name=NAME category affect name=”unpleasant” thatMY NAME IS that patternYOUR pattern templaterandom lisetconcernpleasantsetconcern I am sorry, but tell me your name.li lisetconcernunpleasantsetconcern I am sorry, tell me what happened.li random template affectcategory ... Figure 21. Example units in the AIML database. Finally, the system extracts the dialog emotional situation. For this purpose, we added two labels in the AIML scheme see figure 3: 1 a label to distinct a user’s emotional situation “affect”; and 2 a label to distinct the system’s emotional situation “concern”. These labels describe a type of a valance neutral, pleasant or unpleasant or a sign of a joke. By these additional tags, the input pattern matching operation searches first then not only in the same conversation topic and the same history pattern, but also in the same user’s emotional situation. By this way, the tag also indicates the conversation’s emotional situation.

3.2. Emotion Expression Experiment