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Question Aware Open Information Extraction (Question aware Open IE) takesquestion and passage as inputs, outputting an answer tuple which contains asubject, a predicate, and one or more arguments. Each field of answer is anatural language word sequence and is extracted from the passage. Thesemi-structured answer has two advantages which are more readable andfalsifiable compared to span answer. There are two approaches to solve thisproblem. One is an extractive method which extracts candidate answers from thepassage with the Open IE model, and ranks them by matching with questions. Itfully uses the passage information at the extraction step, but the extractionis independent to the question. The other one is the generative method whichuses a sequence to sequence model to generate answers directly. It combines thequestion and passage as input at the same time, but it generates the answerfrom scratch, which does not use the facts that most of the answer words comefrom in the passage. To guide the generation by passage, we present a two-stagedecoding model which contains a tagging decoder and a correction decoder. Atthe first stage, the tagging decoder will tag keywords from the passage. At thesecond stage, the correction decoder will generate answers based on taggedkeywords. Our model could be trained end-to-end although it has two stages.Compared to previous generative models, we generate better answers bygenerating coarse to fine. We evaluate our model on WebAssertions (Yan et al.,2018) which is a Question aware Open IE dataset. Our model achieves a BLEUscore of 59.32, which is better than previous generative methods.

Tag and Correct: Question aware Open Information Extraction with Two-stage Decoding