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Neural generative models have been become increasingly popular when buildingconversational agents. They offer flexibility, can be easily adapted to newdomains, and require minimal domain engineering. A common criticism of thesesystems is that they seldom understand or use the available dialog historyeffectively. In this paper, we take an empirical approach to understanding howthese models use the available dialog history by studying the sensitivity ofthe models to artificially introduced unnatural changes or perturbations totheir context at test time. We experiment with 10 different types ofperturbations on 4 multi-turn dialog datasets and find that commonly usedneural dialog architectures like recurrent and transformer-based seq2seq modelsare rarely sensitive to most perturbations such as missing or reorderingutterances, shuffling words, etc. Also, by open-sourcing our code, we believethat it will serve as a useful diagnostic tool for evaluating dialog systems inthe future.

Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study