Part 1. Definition
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Part 2. Example
Questions are gathered from anonymized, aggregated queries to the Google search engine. Queries that are likely to be yes/no questions are heuristically identified: we found selecting queries where the first word is in a manually constructed set of indicator words and are of sufficient length, to be effective.

Questions are only kept if a Wikipedia page is returned as one of the first five results, in which case the question and Wikipedia page are given to a human annotator for further processing.

Annotators label question/article pairs in a three-step process. First, they decide if the question is good, meaning it is comprehensible, unambiguous, and requesting factual information. This judgment is made before the annotator sees the Wikipedia page. Next, for good questions, annotators find a passage within the document that contains enough information to answer the question. Annotators can mark questions as "not answerable" if the Wikipedia article does not contain the requested information. Finally, annotators mark whether the question's answer is "yes" or "no". Annotating data in this manner is quite expensive since annotators need to search entire Wikipedia documents for relevant evidence and read the text carefully.
Answer: how was the dataset built?
Explanation: This is a good question, and it is answerable based on the context.
Part 3. Exercise
Baselines. As none of the existing KBC methods can solve the OKBC problem, we choose various versions of LiLi as baselines.

Single: Version of LiLi where we train a single prediction model INLINEFORM0 for all test relations.

Sep: We do not transfer (past learned) weights for initializing INLINEFORM0 , i.e., we disable LL.

F-th): Here, we use a fixed prediction threshold 0.5 instead of relation-specific threshold INLINEFORM0 .

BG: The missing or connecting links (when the user does not respond) are filled with "@-RelatedTo-@" blindly, no guessing mechanism.

w/o PTS: LiLi does not ask for additional clues via past task selection for skillset improvement.
Answer:
What baseline is used in the experiments?