SlowGuess/ACL_2020_OCR huggingface dataset:
$x$ , we pass it into the model  $D_z \circ E$  and obtain a canonical utterance  $\hat{z}$  via greedy decoding. Then  $\hat{z}$  is forwarded into the dual paraphrase model  $D_x \circ E$ . By measuring the BLEU score between raw input  $x$  and reconstructed utterance  $\hat{x}$ , we obtain one metric  $BLEU(x, \hat{x})$ . In the reverse path, we will obtain another metric by calculating the overall accuracy between raw canonical utterance  $z$  and its reconstructed version  $\hat{z}$  through the naive semantic parser  $P_{nsp}$ . The overall metric for model selection is ( $\lambda$  is a scaling hyper-parameter, set to 4 in our experiments)
$$
\begin{array}{l} \operatorname {M e t r i c} \left(\mathcal {X} _ {d e v}, \mathcal {Z} _ {d e v}\right) = \lambda \cdot \mathbb {E} _ {x \sim \mathcal {X} _ {d e v}} \left[ B L E U (x, \hat {x}) \right] \\ + \mathbb {E} _ {z \sim \mathcal {Z} _ {d e v}} [ \mathbb {I} \cdot \{P _ {n s p} (z) = P _ {n s p} (\hat {z}) \} ] \\ \end{array}
$$
Auxiliary models The naive semantic parser  $P_{nsp}$  is another Seq2Seq model with exactly the same architecture as  $D_z \circ E$ . We do not incorporate copy mechanism cause it has been proven useless on dataset OVERNIGHT (Jia and Liang, 2016). The language models  $LM_x$  and  $LM_z$  are all single-layer unidirectional LSTM networks. As for style discriminator  $P_{dis}$ , we use a CNN based sentence classifier (Kim, 2014). We use rectified linear units and filter windows of 3, 4, 5 with 10, 20, 30 feature maps respectively. All the auxiliary models are trained with maximum epochs 100.
For all models discussed above, the embedding dimension is set to 100, hidden size to 200, dropout rate between layers to 0.5. All parameters except embedding layers are initialized by uniformly sampling within the interval  $[-0.2, 0.2]$ .
6817