Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables

We explore neural network models for answering multi-step reasoning questions that operate on semi-structured tables. Challenges arise from deep logical compositionality and domain openness. Our approach is weakly supervised, trained on question-answer-table triples. It generates human readable logical forms from natural language questions, which are then ranked based on word and character convolutional neural networks. A model ensemble achieved at the moment of publication state-of-the-art score on the WikiTableQuestions dataset.