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Though generative dialogue modeling is widely seen as a language modelingtask, the task demands an agent to have a complex natural languageunderstanding of its input text to carry a meaningful interaction with an user.The automatic metrics used evaluate the quality of the generated text as aproxy to the holistic interaction of the agent. Such metrics were earlier shownto not correlate with the human judgement. In this work, we observe that humanevaluation of dialogue agents can be inconclusive due to the lack of sufficientinformation for appropriate evaluation. The automatic metrics are deterministicyet shallow and human evaluation can be relevant yet inconclusive. To bridgethis gap in evaluation, we propose designing a set of probing tasks to evaluatedialogue models. The hand-crafted tasks are aimed at quantitatively evaluatinga generative dialogue model's understanding beyond the token-level evaluationon the generated text. The probing tasks are deterministic like automaticmetrics and requires human judgement in their designing; benefiting from thebest of both worlds. With experiments on probe tasks we observe that, unlikeRNN based architectures, transformer model may not be learning to comprehendthe input text despite its generated text having higher overlap with the targettext.

How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics