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.

[EX Q]: We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.
[EX A]: How was performance of previously proposed rules at very large scale?

[EX Q]: Our corpus comprises a total of 6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities. 
[EX A]: How large is the dataset?

[EX Q]: Input of the model is the concatenation of word embedding and another embedding indicating whether this word is predicate: $ \mathbf {x}_t = [\mathbf {W}_{\text{emb}}(w_t), \mathbf {W}_{\text{mask}}(w_t = v)]. $
[EX A]:
What's the input representation of OpenIE tuples into the model?