We present assertion based question answering (ABQA), an open domain question
answering task that takes a question and a passage as inputs, and outputs a
semi-structured assertion consisting of a subject, a predicate and a list of
arguments. An assertion conveys more evidences than a short answer span in
reading comprehension, and it is more concise than a tedious passage in
passage-based QA. These advantages make ABQA more suitable for human-computer
interaction scenarios such as voice-controlled speakers. Further progress
towards improving ABQA requires richer supervised dataset and powerful models
of text understanding. To remedy this, we introduce a new dataset called
WebAssertions, which includes hand-annotated QA labels for 358,427 assertions
in 55,960 web passages. To address ABQA, we develop both generative and
extractive approaches. The backbone of our generative approach is sequence to
sequence learning. In order to capture the structure of the output assertion,
we introduce a hierarchical decoder that first generates the structure of the
assertion and then generates the words of each field. The extractive approach
is based on learning to rank. Features at different levels of granularity are
designed to measure the semantic relevance between a question and an assertion.
Experimental results show that our approaches have the ability to infer
question-aware assertions from a passage. We further evaluate our approaches by
incorporating the ABQA results as additional features in passage-based QA.
Results on two datasets show that ABQA features significantly improve the
accuracy on passage-based~QA.

本文介绍了一种基于断言的问答（ABQA）任务，旨在回答问题而不是生成短答案，并提出了一种新的手动注释的数据集（WebAssertions）。我们采用生成和抽取两种方法来解决 ABQA，并将其应用于基于预测和排名的 QA 任务中，实验结果表明，ABQA 功能可以显著提高传统 QA 任务的准确性。