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.

To compare our neural models with the traditional approaches, we experimented with a number of existing models including: Support Vector Machine (SVM), a discriminative max-margin model; Logistic Regression (LR), a discriminative probabilistic model; and Random Forest (RF), an ensemble model of decision trees.
what was their baseline comparison?

Our Reddit data was gathered using Reddit's public API, collecting the most recent jokes. Every time the scraper ran, it also updated the upvote score of the previously gathered jokes. This data collection occurred every hour through the months of March and April 2019. Since the data was already split into body and punchline sections from Reddit, we created separate datasets containing the body of the joke exclusively and the punchline of the joke exclusively. Additionally, we created a dataset that combined the body and punchline together.

Some sample jokes are shown in Table 1, above. The distribution of joke scores varies wildly, ranging from 0 to 136,354 upvotes. We found that there is a major jump between the 0-200 upvote range and the 200 range and onwards, with only 6% of jokes scoring between 200-20,000. We used this natural divide as the cutoff to decide what qualified as a funny joke, giving us 13884 not-funny jokes and 2025 funny jokes.
How they evaluate if joke is humorous or not?

KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocaw CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish.
How was the KGR10 corpus created?