time synchronously with KLA, as the soft label provided by the labeling network is not that good in the early stages. We tune several hyper-parameters on the validation sets and measure the performances on the test sets for all baselines and our proposed method.

Results
-------

### Evaluation Metrics

In “one source, multiple targets” setting, for an input $c$, given $N$ hypothesis responses $h_i$ generated by a model and $M_c$ reference responses $r_j$, accuracy and diversity are two sides of the generations we need to concern. Automatic quantitative measures for these purposes are still an open research challenge [@liunot; @tong2018one]. [@zhao2017learning] proposed BLEU-precision and BLEU-recall metrics for discourse-level accuracy and diversity respectively as following: $$\begin{split}
\mathsf{precision(c)} &= \frac{\sum_{i=1}^{N}\max_{j \in [1,M_c]}d(r_j, h_i)}{N} \\
\mathsf{recall(c)} &= \frac{\sum_{j=1}^{M_c}\max_{i \in [1,N]}d(r_j, h_i)}{M_c}
\end{split}$$ BLEU-1, BLEU-2 and BLEU-3 are adopted and their average is calculated as the metrics. However, BLEU-recall is defined based on lexical similarity, which might penalize a reasonable but not same prediction. Following [@li2016diversity], we also use the number of *distinct* *n-gram* to measure the word-level diversity. The *distinct* is normalized to \[0, 1\] by dividing the total number of generated tokens. In summary, BLEU-precision is reported as the accuracy measure, and 