Classification-based strategies for combining multiple 5-w question answering systems
Classification-based strategies for combining multiple 5-w question answering systems
复制标题
基于分类的组合多个5-w问答系统的策略
DOI：
10.21437/interspeech.2009-691
复制
发表时间：
2009
期刊：
影响因子：
4.3
通讯作者：
Kartavya Sharma
中科院分区：
文献类型：
--
作者：
Sibel Yaman;Dilek Z. Hakkani;Gökhan Tür;R. Grishman;M. Harper;K. McKeown;Adam Meyers;Kartavya Sharma
我们描述和分析推理策略，结合多个问答系统，其中每个独立开发的输出。具体来说，我们解决了DARPA资助的GALE信息蒸馏第3年的任务，即为每个给定的句子找到5-Wh问题（谁，什么，何时，何地和为什么）的答案。我们采用的方法围绕着使用判别式学习来确定最佳系统。特别是，我们用一组新的特征来训练支持向量机，这些特征编码了系统返回尽可能多的正确答案的能力。我们分析了两种组合策略：一种是在句子的粒度上组合多个系统，另一种是在单个字段的粒度上组合多个系统。我们的实验结果表明，所提出的功能和组合策略能够提高整体性能的22%至36%相对于随机选择，16%至35%相对于多数表决方案，15%至23%相对于最好的个人系统。索引术语：问答，口语理解系统
We describe and analyze inference strategies for combining outputs from multiple question answering systems each of which was developed independently. Specifically, we address the DARPA-funded GALE information distillation Year 3 task of finding answers to the 5-Wh questions (who, what, when, where, and why) for each given sentence. The approach we take revolves around determining the best system using discriminative learning. In particular, we train support vector machines with a set of novel features that encode systems’ capabilities of returning as many correct answers as possible. We analyze two combination strategies: one combines multiple systems at the granularity of sentences, and the other at the granularity of individual fields. Our experimental results indicate that the proposed features and combination strategies were able to improve the overall performance by 22% to 36% relative to a random selection, 16% to 35% relative to a majority voting scheme, and 15% to 23% relative to the best individual system. Index Terms: Question answering, Systems for spoken language understanding