Attributing answer text to its source document for information-seeking
questions is crucial for building trustworthy, reliable, and accountable
systems. We formulate a new task of post-hoc answer attribution for long
document comprehension (LDC). Owing to the lack of long-form abstractive and
information-seeking LDC datasets, we refactor existing datasets to assess the
strengths and weaknesses of existing retrieval-based and proposed answer
decomposition and textual entailment-based optimal selection attribution
systems for this task. We throw light on the limitations of existing datasets
and the need for datasets to assess the actual performance of systems on this
task.

长文理解中对答案文本进行归因对于建立可信赖、可靠和负责任的系统至关重要，而缺乏长文崭新性和信息查找性的归因数据集使得我们需要重构现有数据集以评估现有基于检索和提出的答案分解和文本蕴含为基础的最佳选择归因系统在这一任务上的优势和不足，同时也突显了现有数据集的限制和对于评估系统在该任务上实际性能的需求。

面向可信、可靠的长文档理解的事后答案归因：任务、洞见和挑战

Post-Hoc Answer Attribution for Grounded and Trustworthy Long Document  Comprehension: Task, Insights, and Challenges

Open-domain human-computer conversation has attracted much attention in the
field of NLP. Contrary to rule- or template-based domain-specific dialog
systems, open-domain conversation usually requires data-driven approaches,
which can be roughly divided into two categories: retrieval-based and
generation-based systems. Retrieval systems search a user-issued utterance
(called a query) in a large database, and return a reply that best matches the
query. Generative approaches, typically based on recurrent neural networks
(RNNs), can synthesize new replies, but they suffer from the problem of
generating short, meaningless utterances. In this paper, we propose a novel
ensemble of retrieval-based and generation-based dialog systems in the open
domain. In our approach, the retrieved candidate, in addition to the original
query, is fed to an RNN-based reply generator, so that the neural model is
aware of more information. The generated reply is then fed back as a new
candidate for post-reranking. Experimental results show that such ensemble
outperforms each single part of it by a large margin.

本文提出了一种将检索式和生成式对话系统相结合的方法，将检索到的候选回答与原始查询一起馈送到基于循环神经网络的回答生成器中，以使神经模型了解更多信息。 实验结果表明，这种集成方法明显优于单独使用检索和生成方法。