论文标题
会话问题回答段落，通过利用单词接近网络
Conversational Question Answering over Passages by Leveraging Word Proximity Networks
论文作者
论文摘要
在文本段落上的问题回答（QA）是对信息检索的长期兴趣的问题。最近，对话环境引起了人们的关注，用户提出了一系列问题，以满足她围绕主题的信息需求。尽管这种设置是一种自然的设置，并且与人类之间的交流相似，但它引入了两个关键的研究挑战：了解用户在后续问题中所隐含的上下文，并处理临时问题的表述。在这项工作中，我们演示了皇冠（通过文字网络的推理来对话段落排名）：一种无监督但有效的对话质量质量质量响应的系统，它支持多种环境传播模式在多个转弯中。为此，Crown首先构建了一个单词接近网络（WPN），从大型语料库到存储具有统计学意义的术语共发生。在回答时间，通过与问题相似性的组合以及查询术语的相似性来对段落进行排名：这些因素是通过从WPN中读取节点和边缘权重来衡量的。 Crown提供了一个既可以直观的界面，又是最终用户的直观，也提供了专家对各个设置的重新配置的见解。对TREC铸造数据进行了评估，在该数据中，它在神经方法库中实现了高于米迪的性能。
Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans conversing with each other, it introduces two key research challenges: understanding the context left implicit by the user in follow-up questions, and dealing with ad hoc question formulations. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. To this end, CROWN first builds a word proximity network (WPN) from large corpora to store statistically significant term co-occurrences. At answering time, passages are ranked by a combination of their similarity to the question, and coherence of query terms within: these factors are measured by reading off node and edge weights from the WPN. CROWN provides an interface that is both intuitive for end-users, and insightful for experts for reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.
