  We introduce the first system towards the novel task of answering complex
multisentence recommendation questions in the tourism domain. Our solution uses
a pipeline of two modules: question understanding and answering. For question
understanding, we define an SQL-like query language that captures the semantic
intent of a question; it supports operators like subset, negation, preference
and similarity, which are often found in recommendation questions. We train and
compare traditional CRFs as well as bidirectional LSTM-based models for
converting a question to its semantic representation. We extend these models to
a semisupervised setting with partially labeled sequences gathered through
crowdsourcing. We find that our best model performs semi-supervised training of
BiDiLSTM+CRF with hand-designed features and CCM(Chang et al., 2007)
constraints. Finally, in an end to end QA system, our answering component
converts our question representation into queries fired on underlying knowledge
sources. Our experiments on two different answer corpora demonstrate that our
system can significantly outperform baselines with up to 20 pt higher accuracy
and 17 pt higher recall.
