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Báo cáo khoa học: "Flexible Answer Typing with Discriminative Preference Ranking"

An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What .? and Which .? questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost 0.2 in MRR for both the first appropriate. | Flexible Answer Typing with Discriminative Preference Ranking Christopher Pinchak Dekang Lin Davood Rafiei Department of Computing Science University of Alberta Edmonton Alberta Canada pinchak drafiei @cs.ualberta.ca Google Inc. 1600 Amphitheatre Parkway Mountain View CA USA lindek@google.com Abstract An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What . and Which . questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost 0.2 in MRR for both the first appropriate and first correct answers is observed along with an increase in precision over the entire range of recall. 1 Introduction Question answering QA systems have received a great deal of attention because they provide both a natural means of querying via questions and because they return short concise answers. These two advantages simplify the task of finding information relevant to a topic of interest. Questions convey more than simply a natural language query an implicit expectation of answer type is provided along with the question words. The discovery and exploitation of this implicit expected type is called answer typing. We introduce an answer typing method that is sufficiently flexible to use a wide variety of features while at the same time providing a high level of performance. Our answer typing method avoids the use of pre-determined classes that are often lacking for unanticipated answer types. Because answer typing is only part of the QA task a flexible answer typing model ensures that answer typing can be easily and usefully incorporated into a .