Abstract

We propose a novel machine-learning framework for dialogue modeling which uses representations based on hash functions. More specifically, each person's response is represented by a binary hashcode where each bit reflects presence or absence of a certain text pattern in the response. Hashcodes serve as compressed text representations, allowing for efficient similarity search. Moreover, hashcode of one person's response can be used as a feature vector for predicting the hashcode representing another person's response. The proposed hashing model of dialogue is obtained by maximizing a novel lower bound on the mutual information between the hashcodes of consecutive responses. We apply our approach in psychotherapy domain evaluating its effectiveness on a real-life dataset consisting of therapy sessions with patients suffering from depression; in addition, we also model transcripts of interview …

Date

July 13, 2018

Authors

Sahil Garg, Guillermo Cecchi, Irina Rish, Shuyang Gao, Greg Ver Steeg, Sarik Ghazarian, Palash Goyal, Aram Galstyan

Conference

Linguistic and Cognitive Approaches To Dialog Agents Workshop

Publisher

CEUR-WS