At Clippers Tuesday, Evan Jaffe will presenting work in progress using Sequential Matching Networks to do dialogue response selection.
https://arxiv.org/abs/1612.01627
SMN architecture is designed to maintain dialogue history (using an RNN) and thus provide extended context. The task is formulated as ranking a set of k candidate responses, given a dialogue history. Preliminary results on a virtual patient dataset show good ranking accuracy (95% on dev) when the network chooses between the true next response, and 9 randomly selected negative examples. However, this task may be too easy, so a few more challenging tests are worth exploring, including increasing the size of k and choosing more confusable candidates. An n-gram overlap could be a good baseline. Ultimately, using the SMN to rerank an n-best list coming from a CNN model (Jin et al 2017) could prove beneficial, complementing the CNN with an ability to track previous turns. This history could be useful for questions with zero anaphora like, ‘What dose’, which crucially rely on previous turns for successful interpretation.