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.

Q: Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: How is a "chunk of posts" defined in this work?

A: chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account
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Q: Shared Self-attention Layers As our model providing two outputs from one input, there is a bifurcation setting for how much shared part should be determined. Both constituent and dependency parsers share token representation and 8 self-attention layers at most. Assuming that either parser always takes input information flow through 8 self-attention layers as shown in Figure FIGREF4, then the number of shared self-attention layers varying from 0 to 8 may reflect the shared degree in the model. When the number is set to 0, it indicates only token representation is shared for both parsers trained for the joint loss through each own 8 self-attention layers. When the number is set to less than 8, for example, 6, then it means that both parsers first shared 6 layers from token representation then have individual 2 self-attention layers. For different numbers of shared layers, the results are in Table TABREF14. We respectively disable the constituent and the dependency parser to obtain a separate learning setting for both parsers in our model. The comparison in Table TABREF14 indicates that even though without any shared self-attention layers, joint training of our model may significantly outperform separate learning mode. At last, the best performance is still obtained from sharing full 8 self-attention layers. 
 Question: How are different network components evaluated?

A: For different numbers of shared layers, the results are in Table TABREF14. We respectively disable the constituent and the dependency parser to obtain a separate learning setting for both parsers in our model. 
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Q: We use data from Mandarin Chinese and Cantonese. For each language, the data consists of a list of spoken words, recorded by the same speaker. The Mandarin dataset is from a female speaker and is provided by Shtooka, and the Cantonese dataset is from a male speaker and is downloaded from Forvo, an online crowd-sourced pronunciation dictionary. 
 Question: What dataset is used for training?

A:
Mandarin dataset Cantonese dataset
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