TED-KISS: A Known-Item Speech Video Search Benchmark

Known-item search is an everyday natural scenario that we search for a specific thing (maybe a song) while only remembering some details about it. Existing benchmarks generally focus on brief user requests which specify some metadata like the title, or the time. However, in most cases, the users can hardly recall such information accurately. In order to embrace the research of known-item search, we present a new publicly available known-item speech video search benchmark, namely TED-KISS, which takes TED talks as an example. The video collection is constructed with up-to-date nearly 80,000 TED and TEDx talks on Youtube. These talks cover various topics, and their titles, speakers, descriptions, full-text subtitles, as well as original links are extracted as metadata, which makes the researches on text-based retrieval and multimedia retrieval feasible. Unlike other benchmarks concerning visual contents in segments, the user requests in TED-KISS are generated through a more natural process, partly through original related topics posted on Reddit and Baidu Tieba, and partly through manual imitative requests annotated by volunteers in a scenario simulation. In addition, we analyze the characteristics of our benchmark through evaluations of several existing text-based IR and Neural-IR models, which also can be served as baselines for this task.

• Publication year

  2018

• Venue

  International Conference on Information and Knowledge Management

• Publication date

  2018-10-17

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/3269206.3269300
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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