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.

Q: As a document classifier we employ a word-based CNN similar to Kim consisting of the following sequence of layers: $ \texttt {Conv} \xrightarrow{} \texttt {ReLU} \xrightarrow{} \texttt {1-Max-Pool} \xrightarrow{} \texttt {FC} \\ $ Future work would include applying LRP to other neural network architectures (e.g. character-based or recurrent models) on further NLP tasks, as well as exploring how relevance information could be taken into account to improve the classifier's training procedure or prediction performance.

A: Do the experiments explore how various architectures and layers contribute towards certain decisions?
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Q: We use 16 different datasets from several popular review corpora used in BIBREF20 . These datasets consist of 14 product review datasets and two movie review datasets. We use CoNLL 2000 BIBREF22 sequence labeling dataset for both POS Tagging and Chunking tasks.

A: What dataset did they use?
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Q: There are 8757 news records in our preprocessed data set. 

A:
How large is the dataset?
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