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:
Baselines. We compare our approach (FacTweet) to the following set of baselines: LR + Bag-of-words: We aggregate the tweets of a feed and we use a bag-of-words representation with a logistic regression (LR) classifier.

Tweet2vec: We use the Bidirectional Gated recurrent neural network model proposed in BIBREF20. We keep the default parameters that were provided with the implementation. To represent the tweets, we use the decoded embedding produced by the model. With this baseline we aim at assessing if the tweets' hashtags may help detecting the non-factual accounts.

LR + All Features (tweet-level): We extract all our features from each tweet and feed them into a LR classifier. Here, we do not aggregate over tweets and thus view each tweet independently.

LR + All Features (chunk-level): We concatenate the features' vectors of the tweets in a chunk and feed them into a LR classifier.

FacTweet (tweet-level): Similar to the FacTweet approach, but at tweet-level; the sequential flow of the tweets is not utilized. We aim at investigating the importance of the sequential flow of tweets.

Top-$k$ replies, likes, or re-tweets: Some approaches in rumors detection use the number of replies, likes, and re-tweets to detect rumors BIBREF21. Thus, we extract top $k$ replied, liked or re-tweeted tweets from each account to assess the accounts factuality. We tested different $k$ values between 10 tweets to the max number of tweets from each account. Figure FIGREF24 shows the macro-F1 values for different $k$ values. It seems that $k=500$ for the top replied tweets achieves the highest result. Therefore, we consider this as a baseline. 
 Question: What baselines were used in this work?
answer:
LR + Bag-of-words Tweet2vec LR + All Features (tweet-level) LR + All Features (chunk-level) FacTweet (tweet-level) Top-$k$ replies, likes, or re-tweets


question:
We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).  
 Question: How did they get relations between mentions?
answer:
Assign a value to the relation based on whether mentions occur in the same document, if mentions are identical, or if mentions are in the same coreference chain.


question:
Paraphrases can be obtained by translating an English string into a foreign language and then back-translating it into English.  
 Question: It looks like learning to paraphrase questions, a neural scoring model and a answer selection model cannot be trained end-to-end. How are they trained?
answer:

using multiple pivot sentences