ABSTRACT

In everyday communication, where-questions are answered by place descriptions. To answer where-questions automatically, computers should be able to generate relevant place descriptions that satisfy inquirers’ information needs. Human-generated answers to where-questions constructed based on a few anchor places that characterize the location of inquired places. The challenge for automatically generating such relevant responses stems from selecting relevant anchor places. In this paper, we present templates that allow to characterize the human-generated answers and to imitate their structure. These templates are patterns of generic geographic information derived and encoded from the largest available machine comprehension dataset, MS MARCO v2.1. In our approach, the toponyms in the questions and answers of the dataset are encoded into sequences of generic information. Next, sequence prediction methods are used to model the relation between the generic information in the questions and their answers. Finally, we evaluate the performance of predicting templates for answers to where-questions.

摘要

在日常交流中，地方说明可以回答问题。为了自动回答哪里的问题，计算机应该能够生成满足查询者信息需求的相关场所描述。人工生成的对问题的答案，这些问题是根据几个锚点构建的，这些锚点代表了所查询位置的位置。自动生成此类相关响应的挑战来自选择相关锚点位置。在本文中，我们介绍了模板可以表征人为生成的答案并模仿其结构。这些模板是从最大的可用机器理解数据集MS MARCO v2.1派生和编码的通用地理信息的模式。在我们的方法中，数据集的问题和答案中的地名被编码为通用信息序列。接下来，使用序列预测方法对问题中的一般信息及其答案之间的关系进行建模。最后，我们评估预测模板的性能，以回答问题。