Question Answering for the Spoken Web (QASW) is an information retrieval evaluation in which the goal was to match spoken Gujarati questions to spoken Gujarati answers. This paper describes the design of the task, the development of the test collection, the system used by the participating teams, the runs that were submitted, and the corresponding results. This paper thus combines the track overview and the participant results.

Research on ranked retrieval of spoken content has assumed the existence of some automated (word or phonetic) transcription. Recently, however, methods have been demonstrated for matching spoken terms to spoken content without the need for language-tuned transcription. This paper describes the first application of such techniques to ranked retrieval, evaluated using a newly created test collection. Both the queries and the collection to be searched are based on Gujarati produced naturally by native speakers; relevance assessment was performed by other native speakers of Gujarati. Ranked retrieval is based on fast acoustic matching that identifies a deeply nested set of matching speech regions, coupled with ways of combining evidence from those matching regions. Results indicate that the resulting ranked lists may be useful for some practical similarity-based ranking tasks.

No abstract

No abstract

No abstract