TASK DEFINITION: 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.
PROBLEM: We evaluate our method on three tagging tasks: POS tagging (Pos), morphological tagging (Morph) and supertagging (Stag). We select these tasks as examples for tagging applications because they differ strongly in tag set sizes. The test results for the three tasks are shown in Table TABREF17 in three groups. The first group of seven columns are the results for Pos, where both LSTM and CNN have three variations of input features: word only ( INLINEFORM0 ), character only ( INLINEFORM1 ) and both ( INLINEFORM2 ). For Morph and Stag, we only use the INLINEFORM3 setting for both LSTM and CNN.
Question: Do they jointly tackle multiple tagging problems?

SOLUTION: No

PROBLEM: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question.
Question: How do they measure performance?

SOLUTION: average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values

PROBLEM: 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?

SOLUTION:
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