On the other hand, we observe the reconstruction model could potentially go beyond surface token-wise match between NL utterances and MRs, promoting more complex (longer) candidate MRs that more adequately encode the semantics of the input utterance 7 (e.g., in Example 3, z 5 receives a much higher reconstruction score, while the difference between the discriminative matching scores is small).
on the challenging CONALA dataset with much weaker alignment between its succinct NL utterances and highly compositional MRs (e.g., Example 4), the matching model does not function as well (e.g., the incorrect MR z 1 received a higher matching score).