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，地点问题由地点描述来回答。为了自动回答地点问题，计算机应该能够生成满足查询者信息需求的相关地点描述。人工生成的地点问题的答案是基于几个锚点构建的，这些锚点表征了所查询地点的位置。自动生成此类相关响应的挑战源于选择相关锚点。在本文中，我们展示了模板允许表征人类生成的答案并模仿它们的结构。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模式。在我们的方法中，数据集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通用信息与其答案之间的关系进行建模。最后，我们评估了预测模板以回答 where-questions 的性能。