Paper
Towards an Automatic Turing Test: Learning to Evaluate Dialogue\n Responses
Automatically evaluating the quality of dialogue responses for unstructured\ndomains is a challenging problem. Unfortunately, existing automatic evaluation\nmetrics are biased and correlate very poorly with human judgements of response\nquality. Yet having an accurate automatic evaluation procedure is crucial for\ndialogue research, as it allows rapid prototyping and testing of new models\nwith fewer expensive human evaluations. In response to this challenge, we\nformulate automatic dialogue evaluation as a learning problem. We present an\nevaluation model (ADEM) that learns to predict human-like scores to input\nresponses, using a new dataset of human response scores. We show that the ADEM\nmodel's predictions correlate significantly, and at a level much higher than\nword-overlap metrics such as BLEU, with human judgements at both the utterance\nand system-level. We also show that ADEM can generalize to evaluating dialogue\nmodels unseen during training, an important step for automatic dialogue\nevaluation.\n
Authors: Lowe, Ryan · Noseworthy, Michael · Serban, Iulian V. · Angelard-Gontier, Nicolas · Bengio, Yoshua · Pineau, Joelle