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.

[EX Q]: Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below. 
 Question: Why did they choose WER as evaluation metric?
[EX A]: WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD WER can be used for initial analyses

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

[EX Q]: Through the experiments, we empirically studied our analysis on DIRL and the effectiveness of our proposed solution in dealing with the problem it suffered from. In addition, we studied the impact of each step described in §SECREF10 and §SECREF14 to our proposed solution, respectively. To performe the study, we carried out performance comparison between the following models:

SO: the source-only model trained using source domain labeled data without any domain adaptation.

CMD: the centre-momentum-based domain adaptation model BIBREF3 of the original DIRL framework that implements $\mathcal {L}_{inv}$ with $\text{CMD}_K$.

DANN: the adversarial-learning-based domain adaptation model BIBREF2 of the original DIRL framework that implements $\mathcal {L}_{inv}$ with $\text{JSD}(\rm {P}_S, \rm {P}_T)$.

$\text{CMD}^\dagger $: the weighted version of the CMD model that only applies the first step (described in §SECREF10) of our proposed method.

$\text{DANN}^\dagger $: the weighted version of the DANN model that only applies the first step of our proposed method.

$\text{CMD}^{\dagger \dagger }$: the weighted version of the CMD model that applies both the first and second (described in §SECREF14) steps of our proposed method.

$\text{DANN}^{\dagger \dagger }$: the weighted version of the DANN model that applies both the first and second steps of our proposed method.

$\text{CMD}^{*}$: a variant of $\text{CMD}^{\dagger \dagger }$ that assigns $\mathbf {w}^*$ (estimate from target labeled data) to $\mathbf {w}$ and fixes this value during model training.

$\text{DANN}^{*}$: a variant of $\text{DANN}^{\dagger \dagger }$ that assigns $\mathbf {w}^*$ to $\mathbf {w}$ and fixes this value during model training. We conducted experiments on the Amazon reviews dataset BIBREF9, which is a benchmark dataset in the cross-domain sentiment analysis field.  From this dataset, we constructed 12 binary-class cross-domain sentiment analysis tasks: B$\rightarrow $D, B$\rightarrow $E, B$\rightarrow $K, D$\rightarrow $B, D$\rightarrow $E, D$\rightarrow $K, E$\rightarrow $B, E$\rightarrow $D, E$\rightarrow $K, K$\rightarrow $B, K$\rightarrow $D, K$\rightarrow $E.  We additionally constructed 12 multi-class cross-domain sentiment classification tasks. Tasks were designed to distinguish reviews of 1 or 2 stars (class 1) from those of 4 stars (class 2) and those of 5 stars (class 3).  Table TABREF27 shows model performance on the 12 binary-class cross-domain tasks. From this table, we can obtain the following observations.  
 Question: How is DIRL evaluated?
[EX A]:
Through the experiments, we empirically studied our analysis on DIRL and the effectiveness of our proposed solution in dealing with the problem it suffered from.