  Question Answering (QA) is fundamental to natural language processing in that
most nlp problems can be phrased as QA (Kumar et al., 2015). Current weakly
supervised memory network models that have been proposed so far struggle at
answering questions that involve relations among multiple entities (such as
facebook's bAbi qa5-three-arg-relations in (Weston et al., 2015)). To address
this problem of learning multi-argument multi-hop semantic relations for the
purpose of QA, we propose a method that combines the jointly learned long-term
read-write memory and attentive inference components of end-to-end memory
networks (MemN2N) (Sukhbaatar et al., 2015) with distributed sentence vector
representations encoded by a Skip-Thought model (Kiros et al., 2015). This
choice to append Skip-Thought Vectors to the existing MemN2N framework is
motivated by the fact that Skip-Thought Vectors have been shown to accurately
model multi-argument semantic relations (Kiros et al., 2015).
