  Recent years have seen growing interest in conversational agents, such as
chatbots, which are a very good fit for automated customer support because the
domain in which they need to operate is narrow. This interest was in part
inspired by recent advances in neural machine translation, esp. the rise of
sequence-to-sequence (seq2seq) and attention-based models such as the
Transformer, which have been applied to various other tasks and have opened new
research directions in question answering, chatbots, and conversational
systems. Still, in many cases, it might be feasible and even preferable to use
simple information retrieval techniques. Thus, here we compare three different
models:(i) a retrieval model, (ii) a sequence-to-sequence model with attention,
and (iii) Transformer. Our experiments with the Twitter Customer Support
Dataset, which contains over two million posts from customer support services
of twenty major brands, show that the seq2seq model outperforms the other two
in terms of semantics and word overlap.
