In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[EX Q]: The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061.
[EX A]: Which dimensionality do they use for their embeddings?

[EX Q]: The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs.
[EX A]: How well did the baseline perform?

[EX Q]: In this case, the decoding function is a linear projection, which is $= f_{\text{de}}()=+ $ , where $\in ^{d_\times d_}$ is a trainable weight matrix and $\in ^{d_\times 1}$ is the bias term. A family of bijective transformation was designed in NICE BIBREF17 , and the simplest continuous bijective function $f:^D\rightarrow ^D$ and its inverse $f^{-1}$ is defined as:

$$h: \hspace{14.22636pt} _1 &= _1, & _2 &= _2+m(_1) \nonumber \\ h^{-1}: \hspace{14.22636pt} _1 &= _1, & _2 &= _2-m(_1) \nonumber $$ (Eq. 15)

where $_1$ is a $d$ -dimensional partition of the input $\in ^D$ , and $m:^d\rightarrow ^{D-d}$ is an arbitrary continuous function, which could be a trainable multi-layer feedforward neural network with non-linear activation functions. It is named as an `additive coupling layer' BIBREF17 , which has unit Jacobian determinant. To allow the learning system to explore more powerful transformation, we follow the design of the `affine coupling layer' BIBREF24 :

$$h: \hspace{5.69046pt} _1 &= _1, & _2 &= _2 \odot \text{exp}(s(_1)) + t(_1) \nonumber \\ h^{-1}: \hspace{5.69046pt} _1 &= _1, & _2 &= (_2-t(_1)) \odot \text{exp}(-s(_1)) \nonumber $$ (Eq. 16)

where $s:^d\rightarrow ^{D-d}$ and $t:^d\rightarrow ^{D-d}$ are both neural networks with linear output units.

The requirement of the continuous bijective transformation is that, the dimensionality of the input $$ and the output $$ need to match exactly.
[EX A]:
What are the two decoding functions?