Evaluation Understudy for Dialogue Coherence Models

Abstract

Evaluating a dialogue system is seen as a major challenge within the dialogue research community. Due to the very nature of the task, most of the evaluation methods need a substantial amount of human involvement. Following the tradition in machine translation, summarization and discourse coherence modeling, we introduce the the idea of evaluation understudy for dialogue coherence models. Following (Lapata, 2006), we use the information ordering task as a testbed for evaluating dialogue coherence models. This paper reports findings about the reliability of the information ordering task as applied to dialogues. We find that simple n-gram co-occurrence statistics similar in spirit to BLEU (Papineni et al., 2001) correlate very well with human judgments for dialogue coherence