question naturalness data set

Implemented earlier for questions that do not take keywords and questions that do take one.

The question that takes two is mada.

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Data 1: 600 questions taking 0 to 1 keywords

Data 2: (unused) 6,000 cases under the same conditions as Data 1

Data 3: Active learning, using data 1 to train the model and selecting only those with 0.1 to 0.9

Data 4: Question text that takes two keywords, with one of them filled in with an X

question naturalness data set

Human input

Questions about it

Unnatural (0)/Natural (1)/Blank (0.5)

Distressed(1)/Not distressed(0)

The training data was gathered in a haphazard way, so the training part was created.

feature value

body (of a machine)

context (of a passage)

position of appearance

I forgot to include it in the original data template.

Keyword, Question ID

Let's just output this one and paste in an additional one.

feature generation

Keyword Body Features

Search input by keyword to find the first occurrence

take up a position with

For questions where keywords are not present, use the entire sentence and the beginning and end of the sentence.

Now I'm asking all the questions for a random choice of sentences.

That's why there are so many of them.

I made 600 cases, but the amount of original text is 13.

Right now, the input text is fixed, and the keywords in it are selected to make question candidates.

But with this, what happens if the keyword doesn't exist in the input, or if the keyword is there but it's not good (like "w").

Should there be an option to say "(go through this input and ask a question about the previous input)"?

Active learning next?

If the ones that are deemed not good enough are no longer used, we won't be able to collect training data.

Trade-offs between use and exploration

Let's just cut corners and go with Epsilon Greedy.

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