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:
In order to verify the effectiveness of our method (i.e., Multi-linear attention) replacing multi-head attention in Transformer, we carry out two NLP tasks named language modeling (LM) and neural machine translation (NMT). Then, we test different model configurations on the PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets and report the results in Table 1 and Table 2 . In this task, we have trained the Transformer model BIBREF2 on WMT 2016 English-German dataset BIBREF36 . 
 Question: What datasets or tasks do they conduct experiments on?
answer:
Language Modeling (LM) PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets neural machine translation (NMT) WMT 2016 English-German dataset


question:
 News Articles
Our dataset source of news articles is described in BIBREF2. This dataset was built from two different sources, for the trusted news (real news) they sampled news articles from the English Gigaword corpus. For the false news, they collected articles from seven different unreliable news sites. Twitter
For this dataset, we rely on a list of several Twitter accounts for each type of false information from BIBREF6. This list was created based on public resources that annotated suspicious Twitter accounts. The authors in BIBREF6 have built a dataset by collecting tweets from these accounts and they made it available.  
 Question: What datasets did they use?
answer:
News Articles Twitter


question:
N-GrAM ranked first in all cases except for the language variety task. In this case, the baseline was the top-ranked system, and ours was second by a small margin. Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task.

 
 Question: On which task does do model do worst?
answer:

Gender prediction task