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]: For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.  
 Question: Along which dimension do the semantically related words take larger values?
[A]: dimension corresponding to the concept that the particular word belongs to


[Q]: 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?
[A]: Language Modeling (LM) PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets neural machine translation (NMT) WMT 2016 English-German dataset


[Q]: Numerically, Our model improves the DAR accuracy over Bi-LSTM-CRF by 2.1% and 0.8% on SwDA and MRDA respectively. 
 Question: By how much do they outperform state-of-the-art solutions on SWDA and MRDA?
[A]:
improves the DAR accuracy over Bi-LSTM-CRF by 2.1% and 0.8% on SwDA and MRDA respectively