Influence relation estimation based on lexical entrainment in conversation

In conversations, people tend to mimic their companions’ behavior depending on their level of trust. This phenomenon is known as entrainment. We propose a probabilistic model for estimating influences among speakers from conversation data involving multiple people by modeling lexical entrainment. The proposed model estimates word use as a function of the weighted sum of the earlier word use of other speakers. The weights represent influences between speakers. The influences can be efficiently estimated by using the expectation maximization (EM) algorithm. We also develop its online inference procedures for sequentially modeling the dynamics of influence relations. Experiments performed on two meeting data sets one in Japanese and one in English demonstrate the effectiveness of the proposed method.

► We propose a probabilistic model for estimating influences among speakers from conversation data. ► We present efficient inference algorithms in both batch and online fashions. ► We analyze influences in real conversation data in English and Japanese. ► We show the effectiveness of the proposed method quantitatively by comparing other methods in terms of perplexity.