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:
We empirically test our approach on a series of experiments on WikiTableQuestions, to our knowledge the only dataset designed for this task. In Algorithm 1 we describe how logical forms are transformed into interpretable textual representations called "paraphrases". 
 Question: What is the source of the paraphrases of the questions?
answer:
WikiTableQuestions


question:
To make the annotated data publicly available, we selected 70 news articles from Arabic WikiNews site. These articles cover recent news from year 2013 to year 2015 in multiple genres (politics, economics, health, science and technology, sports, arts, and culture.) Articles contain 18,300 words, and they are evenly distributed among these 7 genres with 10 articles per each. 
 Question: Where did they collect their dataset from?
answer:

from Arabic WikiNews site