Papers › PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

10 Sep 2021EMNLP 2021 11arXiv:2109.05093archive 2025-07-28

Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau

Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. We propose PICARD (code and trained models available at https://github.com/ElementAI/picard), a method for constraining auto-regressive decoders of language models through incremental parsing. PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step. On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into state-of-the-art solutions.

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ElementAI/picard officialmentioned on GitHubApache-2.0 report
servicenow/picard mentioned on GitHubApache-2.0 report

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is_commonword ElementAI/picard/seq2seq/utils/bridge_content_encoder.py official repository ran Apache-2.0 (permissive) · b724cc2d4066a429 · report
is_number ElementAI/picard/seq2seq/utils/bridge_content_encoder.py official repository ran Apache-2.0 (permissive) · f39fdc1ce6cffe08 · report
is_stopword ElementAI/picard/seq2seq/utils/bridge_content_encoder.py official repository ran Apache-2.0 (permissive) · d56a1f9221823a48 · report
normalize ElementAI/picard/seq2seq/utils/dataset.py official repository ran fingerprinted Apache-2.0 (permissive) · 448eab4c7249a72c · report
cosql_get_input ElementAI/picard/seq2seq/utils/cosql.py official repository unverified Apache-2.0 (permissive) · 96f7360bd80c6f00 · report
spider_get_input ElementAI/picard/seq2seq/utils/spider.py official repository unverified Apache-2.0 (permissive) · b83f0da29c886b71 · report
prepare_splits servicenow/picard/seq2seq/utils/dataset.py community (archive-listed) unverified Apache-2.0 (permissive) · d3c1fa9d710cc4ff · report

Tasks

Dialogue State TrackingSemantic ParsingText to SQLText-To-SQLTranslation

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dialogue State Tracking CoSQL T5-3B + PICARD interaction match accuracy 23.7 #2 of 9 Archive leaderboard report
Dialogue State Tracking CoSQL T5-3B + PICARD question match accuracy 54.6 #2 of 9 Archive leaderboard report
Semantic Parsing spider T5-3B + PICARD Accuracy 71.9 #5 of 10 Archive leaderboard report
Text-To-SQL SPIDER T5-3B+PICARD Exact Match Accuracy (in Dev) 75.5 #1 of 4 Archive leaderboard report
Text-To-SQL SPIDER T5-3B+PICARD Execution Accuracy (in Dev) 79.3 #1 of 4 Archive leaderboard report
Text-To-SQL SPIDER T5-3B Exact Match Accuracy (in Dev) 71.5 #4 of 4 Archive leaderboard report
Text-To-SQL SPIDER T5-3B Execution Accuracy (in Dev) 74.4 #4 of 4 Archive leaderboard report
Text-To-SQL spider T5-3B + PICARD Exact Match Accuracy (Dev) 75.5 #12 of 20 Archive leaderboard report
Text-To-SQL spider T5-3B + PICARD Exact Match Accuracy (Test) 71.9 #12 of 20 Archive leaderboard report
Text-To-SQL spider T5-3B + PICARD Execution Accuracy (Test) 75.1 #12 of 20 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: PICARD

AdafactorAttentionAttention DropoutBPEDense ConnectionsDropoutGated Linear UnitInverse Square Root ScheduleLayer NormalizationLinear LayerMulti-Head AttentionPICARDResidual ConnectionSentencePieceSoftmaxT5

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