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Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering

机译：将证据与概率框架相结合，以进行问题解答中的答案排名和答案合并

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Question answering (QA) aims at finding exact answers to a user's question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA. in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT.
机译：问题解答（QA）旨在从大量文档中找到用户问题的确切答案。大多数QA系统将信息检索与提取技术结合在一起，以识别一组可能的候选者，然后利用某种排名策略来生成最终答案。这种排名过程可能具有挑战性，因为它需要在许多不相关的答案中找出相关的答案。这在多策略质量检查中更具挑战性。其中使用多个应答代理来提取候选应答。由于候选答案来自具有不同分数分布的不同代理，因此如何合并候选答案在答案排名中起着重要作用。在本文中，我们提出了一个统一的概率框架，该框架结合了多种证据来解决答案排名和答案合并方面的挑战。本文的假设是：（1）该框架有效地结合了多种证据，用于识别答案的相关性及其在答案排名中的相关性；（2）该框架支持将答案合并到由多种提取技术返回的候选答案上；（3）该框架可以支持列表问题以及事实问题，（4）该框架可以轻松地应用于其他QA系统，并且（5）该框架可以显着提高QA系统的性能。为了支持我们的假设并证明框架的有效性，进行了广泛的实验。所有的工作大大扩展了Ko等人的初步研究。 （2007a）。在问题回答中选择答案的概率框架。于：NAACL / HLT会议录。

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