instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
question:
We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences.  
 Question: What baselines did they consider?
answer:
LSTM SCIBERT


question:
We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables. 
 Question: Do they add one latent variable for each language pair in their Bayesian model?
answer:
Yes


question:
Our lexical features include 1-, 2-, and 3-grams in both word and character levels. We also use the number of characters and the number of words as features. We then use all the POS tags with their corresponding tf-idf values as our syntactic features and feature values, respectively. Firstly, we employ 300-dimensional pre-trained word embeddings from GloVe BIBREF29 to compute a tweet embedding as the average of the embeddings of words in the tweet. Secondly, we apply the latent semantic indexing BIBREF30 to capture the underlying semantics of the dataset. Here, each tweet is represented as a vector of 100 dimensions. Thirdly, we also extract tweet representation by applying the Brown clustering algorithm BIBREF31 , BIBREF32 —a hierarchical clustering algorithm which groups the words with similar meaning and syntactical function together. Motivated by the verbal irony by means of polarity contrast, such as “I really love this year's summer; weeks and weeks of awful weather”, we use the number of polarity signals appearing in a tweet as the polarity features. The signals include positive words (e.g., love), negative words (e.g., awful), positive emoji icon and negative emoji icon. We use the sentiment dictionaries provided by BIBREF33 to identify positive and negative words in a tweet. We further use boolean features that check whether or not a negation word is in a tweet (e.g., not, n't). 
 Question: What type of lexical, syntactic, semantic and polarity features are used?
answer:

Our lexical features include 1-, 2-, and 3-grams in both word and character levels. number of characters and the number of words POS tags 300-dimensional pre-trained word embeddings from GloVe latent semantic indexing tweet representation by applying the Brown clustering algorithm positive words (e.g., love), negative words (e.g., awful), positive emoji icon and negative emoji icon boolean features that check whether or not a negation word is in a tweet