This document describes an experiment to develop algorithms to predict the interestingness of document fragments. It tested gradient boosted machines and decision tree algorithms on a validation data set to select document passages. Example questions are provided that could be answered by the selected passages along with optional worker feedback that the passages mostly provided answers but some required deeper searching. It then describes training the algorithms on labeled interestingness data and evaluating their performance on predicting interestingness.

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Example Questions:
• How many Swedes speak English as a
percentage?
• When the jominy test was invented?
• Which metals float on water?
• What is oxygen partial pressure at 5000
feet?
• How many Argentine soldiers died in
falklands islands war?
• What ingredients in cough medicine make
you hallucinate?
• How do you say welcome in kashmiri?
• Am I allowed to carry a parachute onboard
as a hand luggage?
• What animal is smaller than a bear but it
eats a plant called bearberry?

12. Worker (optional) feedback:
• "That was pretty interesting and
worked well. I felt like I was able
to get answers to most of the
questions pretty easily."
• "Little confusing at first… search
engines were not very helpful on
most without some in depth
searching"

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27. λ = 0.7

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33. Алгоритмы предсказания
интересности фрагмента:
– GBM
– Decision Tree
Проверка на Validation Set

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