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]: 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]: We also measure the usage of words related to people's core values as reported by Boyd et al. boyd2015. The sets of words, or themes, were excavated using the Meaning Extraction Method (MEM) BIBREF10 .
Question: How do they obtain psychological dimensions of people?
[A]: using the Meaning Extraction Method

[Q]: We began with evaluating standard MT paradigms, i.e., PBSMT BIBREF3 and NMT BIBREF1 . As for PBSMT, we also examined two advanced methods: pivot-based translation relying on a helping language BIBREF10 and induction of phrase tables from monolingual data BIBREF14 .

As for NMT, we compared two types of encoder-decoder architectures: attentional RNN-based model (RNMT) BIBREF2 and the Transformer model BIBREF18 . In addition to standard uni-directional modeling, to cope with the low-resource problem, we examined two multi-directional models: bi-directional model BIBREF11 and multi-to-multi (M2M) model BIBREF8 .

After identifying the best model, we also examined the usefulness of a data augmentation method based on back-translation BIBREF17 .
Question: what was the baseline?
[A]:
pivot-based translation relying on a helping language BIBREF10 nduction of phrase tables from monolingual data BIBREF14 attentional RNN-based model (RNMT) BIBREF2 Transformer model BIBREF18 bi-directional model BIBREF11 multi-to-multi (M2M) model BIBREF8 back-translation BIBREF17