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]: word embedding, input encoder, alignment, aggregation, and prediction
[A]: what is the architecture of the baseline model?

[Q]: We test our proposed approach for binary classification on either sarcasm or irony, on seven benchmark datasets retrieved from different media sources. Below we describe each dataset, please see Table TABREF1 below for a summary. Reddit: BIBREF21 collected SARC, a corpus comprising of 600.000 sarcastic comments on Reddit. We use main subset, SARC 2.0, and the political subset, SARC 2.0 pol. Twitter: We use the Twitter dataset provided for the SemEval 2018 Task 3, Irony Detection in English Tweets BIBREF18 . The dataset was manually annotated using binary labels. We also use the dataset by BIBREF4 , which is manually annotated for sarcasm. Finally, we use the dataset by BIBREF20 , who collected a user self-annotated corpus of tweets with the #sarcasm hashtag. Online Dialogues: We utilize the Sarcasm Corpus V1 (SC-V1) and the Sarcasm Corpus V2 (SC-V2), which are subsets of the Internet Argument Corpus (IAC). Compared to other datasets in our selection, these differ mainly in text length and structure complexity BIBREF22 .
[A]: What are the three different sources of data?

[Q]: Note that we adopt a parsimonious design principle in our modelling: both Centroid and Naive Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$. A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule; A Naive Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;
[A]:
How does the parameter-free model work?