  Recent advances in deep neural networks, language modeling and language
generation have introduced new ideas to the field of conversational agents. As
a result, deep neural models such as sequence-to-sequence, Memory Networks, and
the Transformer have become key ingredients of state-of-the-art dialog systems.
While those models are able to generate meaningful responses even in unseen
situation, they need a lot of training data to build a reliable model. Thus,
most real-world systems stuck to traditional approaches based on information
retrieval and even hand-crafted rules, due to their robustness and
effectiveness, especially for narrow-focused conversations. Here, we present a
method that adapts a deep neural architecture from the domain of machine
reading comprehension to re-rank the suggested answers from different models
using the question as context. We train our model using negative sampling based
on question-answer pairs from the Twitter Customer Support Dataset.The
experimental results show that our re-ranking framework can improve the
performance in terms of word overlap and semantics both for individual models
as well as for model combinations.
