The significance of silence. Long gaps attenuate the preference for ‘yes’ responses in conversation.

In conversation, negative responses to invitations, requests, offers and the like more often occur with a delay – conversation analysts talk of them as dispreferred. Here we examine the contrastive cognitive load ‘yes’ and ‘no’ responses make, either when given relatively fast (300 ms) or delayed (1000 ms). Participants heard minidialogues, with turns extracted from a spoken corpus, while having their EEG recorded. We find that a fast ‘no’ evokes an N400-effect relative to a fast ‘yes’, however this contrast is not present for delayed responses. This shows that an immediate response is expected to be positive – but this expectation disappears as the response time lengthens because now in ordinary conversation the probability of a ‘no’ has increased. Additionally, however, 'No' responses elicit a late frontal positivity both when they are fast and when they are delayed. Thus, regardless of the latency of response, a ‘no’ response is associated with a late positivity, since a negative response is always dispreferred and may require an account. Together these results show that negative responses to social actions exact a higher cognitive load, but especially when least expected, as an immediate response

Domain-sensitive topic management in a modular conversational agent framework

Flexible nontask-oriented conversational agents require content for generating responses and mechanisms that serve them for choosing appropriate topics to drive interactions with users. Structured knowledge resources such as ontologies are a useful mechanism to represent conversational topics. In order to develop the topic-management mechanism, we addressed a number of research issues related to the development of the required infrastructure. First, we address the issue of heavy human involvement in the construction of knowledge resources by proposing a four-stage automatic process for building domain-specific ontologies. These ontologies are comprised of a set of subtaxonomies obtained from WordNet, an electronic dictionary that arranges concepts in a hierarchical structure. The roots of these subtaxonomies are obtained from Wikipedia’s article links or wikilinks; this under the hypothesis that wikilinks provide a sense of relatedness from the article consulted to their destinations. With the knowledge structures defined, we explore the possibility of using semantic relatedness over these domain-specific ontologies as a mean to propose conversational topics in a coherent manner. For this, we examine different automatic measures of semantic relatedness to determine which correlates with human judgements obtained from an automatically constructed dataset. We then examine the question of whether domain information influences the human perception of semantic relatedness in a way that automatic measures do not replicate. This study requires us to design and implement a process to build datasets with pairs of concepts as those used in the literature to evaluate automatic measures of semantic relatedness, but with domain information associated. This study shows, to statistical significance, that existing measures of semantic relatedness do not take domain into consideration, and that including domain as a factor in this calculation can enhance the agreement of automatic measures with human assessments. Finally, this artificially constructed measure is integrated into the Toy’s dialogue manager, in order to help in the real-time selection of conversational topics. This supplements our result that the use of semantic relatedness seems to produce more coherent and interesting topic transitions than existing mechanisms