The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height \leq t. Our parser enjoys several benefits compared to top-down autoregressive parsing. First, since sub-trees in each decoding step are generated in parallel, the theoretical runtime is logarithmic rather than linear. Second, our bottom-up approach learns representations with meaningful semantic sub-programs at each step, rather than semantically vague partial trees. Last, SmBoP includes Transformer-based layers that contextualize sub-trees with one another, allowing us, unlike traditional beam-search, to score trees conditioned on other trees that have been previously explored. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP is competitive with top-down autoregressive parsing. On the test set, SmBoP obtains an EM score of 60.5%DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract \sim5x 
The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height \leq t. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP %DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract leads to a 2.2x speed-up in decoding time and a\sim5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GraPPa.
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First, since
From an efficiency perspective, bottom-up parsing allows to decode all
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in each decoding step are generated in parallel, the theoretical runtime is logarithmic
of a certain height in parallel, leading to logarithmic runtime complexity
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Second, our
From a modeling perspective, a
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approach learns representations with
parser learns representations for
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semantically vague
for semantically-vacuous
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Last, SmBoP includes Transformer-based layers that contextualize sub-trees with one another, allowing us, unlike traditional beam-search, to score trees conditioned on other trees that have been previously explored.
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is competitive with top-down autoregressive parsing. On the test set, SmBoP obtains an EM score of 60.5
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leads to a 2.2x speed-up in decoding time and a
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speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GraPPa.