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 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.

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

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 ≤ 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 leads to a 2.2x speed-up in decoding time and a ~5x 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.

/pdf/smbop-semi-autoregressive-bottom-up-semantic-parsing-1bbv75fxxe.pdf

SmBoP: Semi-autoregressive Bottom-up Semantic Parsing

This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to distinguish local and non-local relations for each node. To this end, we propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying relational features without constructing meta-paths. By virtue of the line graph, messages propagate more efficiently through not only connections between nodes, but also the topology of directed edges. Furthermore, both local and non-local relations are integrated distinctively during the graph iteration. We also design an auxiliary task called graph pruning to improve the discriminative capability of the encoder. Our framework achieves state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the cross-domain text-to-SQL benchmark Spider at the time of writing.

/pdf/lgesql-line-graph-enhanced-text-to-sql-model-with-mixed-52pbxkbk0h.pdf

LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations

Recent years pre-trained language models hit a success on modeling natural language sentences and (semi-)structured tables. However, existing table pre-training techniques always suffer from low data quality and low pre-training efficiency. In this paper, we show that table pre-training can be realized by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. By pre-training on the synthetic corpus, our approach TAPEX dramatically improves the performance on downstream tasks, boosting existing language models by at most 19.5%. Meanwhile, TAPEX has remarkably high pre-training efficiency and yields strong results when using a small pre-trained corpus. Experimental results demonstrate that TAPEX outperforms previous table pre-training approaches by a large margin, and our model achieves new state-of-the-art results on four well-known datasets, including improving the WikiSQL denotation accuracy to 89.6% (+4.9%), the WikiTableQuestions denotation accuracy to 57.5% (+4.8%), the SQA denotation accuracy to 74.5% (+3.5%), and the TabFact accuracy to 84.6% (+3.6%). Our work opens the way to reason over structured data by pre-training on synthetic executable programs.

TAPEX: Table Pre-training via Learning a Neural SQL Executor.

Converting natural language text into executable SQL queries significantly impacts the healthcare domain, specifically when applied to electronic medical records. Given that electronic medical records store extensive patient information in a relational multitable database, developing a Text-to-SQL parser would enable the correlation of intricate medical terminology through semantic parsing. A major challenge is designing a versatile Text2SQL parser applicable to new databases. A critical step towards this goal involves schema linking - accurately identifying references to previously unseen columns or tables during SQL creation. In response to these key challenges, we propose a novel framework—Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records (LI-EMRSQL). This model leverages the Poincaré distance metric detection procedure, utilizing induced relations to enhance the performance of pre-existing graph-based parsers and improve schema linkage. To enhance the generalizability of LI-EMRSQL, the detection process is completely unsupervised and does not necessitate additional parameters. On two conventional Text2SQL datasets and two EMRs Text2SQL datasets, the system delivers SOTA performance. Furthermore, notable enhancements in the model's comprehension and alignment of schemas are observed.

LI-EMRSQL: Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM). However, based on a pilot study, we observe three issues of existing general-purpose language models when they are applied to text-to-SQL semantic parsers: fail to detect column mentions in the utterances, fail to infer column mentions from cell values, and fail to compose complex SQL queries. To mitigate these issues, we present a model pre-training framework, Generation-Augmented Pre-training (GAP), that jointly learns representations of natural language utterances and table schemas by leveraging generation models to generate pre-train data. GAP MODEL is trained on 2M utterance-schema pairs and 30K utterance-schema-SQL triples, whose utterances are produced by generative models. Based on experimental results, neural semantic parsers that leverage GAP MODEL as a representation encoder obtain new state-of-the-art results on both SPIDER and CRITERIA-TO-SQL benchmarks.

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train powerful language models with self-supervised learning objectives, such as Masked Language Model (MLM). Based on a pilot study, we observe three issues of existing general-purpose language models when they are applied in the text-to-SQL semantic parsers: fail to detect the column mentions in the utterances, to infer the column mentions from the cell values, and to compose target SQL queries when they are complex. To mitigate these issues, we present a model pretraining framework, Generation-Augmented Pre-training (GAP), that jointly learns representations of natural language utterance and table schemas, by leveraging generation models to generate high-quality pre-train data. GAP Model is trained on 2 million utterance-schema pairs and 30K utterance-schema-SQL triples, whose utterances are generated by generation models. Based on experimental results, neural semantic parsers that leverage GAP Model as a representation encoder obtain new state-of-the-art results on both Spider and Criteria-to-SQL benchmarks.

/pdf/learning-contextual-representations-for-semantic-parsing-xay9cxuhsz.pdf

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Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training