Understanding Complex Multi-sentence Entity seeking Questions (original) (raw)

We present the novel task of understanding multi-sentence entity-seeking questions (MSEQs) i.e, questions that may be expressed in multiple sentences, and that expect one or more entities as an answer. We formulate the problem of understanding MSEQs as a semantic labeling task over an open representation that makes minimal assumptions about schema or ontology specific semantic vocabulary. At the core of our model, we use a BiDiLSTM (bi-directional LSTM) CRF and to overcome the challenges of operating with low training data, we supplement it by using hand-designed features, as well as hard and soft constraints spanning multiple sentences. We find that this results in a 6-7pt gain over a vanilla BiDiLSTM CRF. We demonstrate the strengths of our work using the novel task of answering real-world entity-seeking questions from the tourism domain. The use of our labels helps answer 53% more questions with 42 % more accuracy as compared to baselines.