Related papers: T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing

T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing

URL: http://arxiv.org/abs/2306.08368v1
Date: Wed, 14 Jun 2023 08:57:13 GMT
Title: T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
Authors: Yuntao Li and Zhenpeng Su and Yutian Li and Hanchu Zhang and Sirui Wang and Wei Wu and Yan Zhang
Abstract summary: seq2seq semantics face much more challenges, including poor quality on schematical information prediction. This paper proposes a seq2seq-oriented decoding strategy called SR, which includes a new intermediate representation S and a reranking method with score re-estimator.
Score: 8.363108209152111
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently. However, compared with abstract-syntactic-tree-based SQL generation, seq2seq semantic parsers face much more challenges, including poor quality on schematical information prediction and poor semantic coherence between natural language queries and SQLs. This paper analyses the above difficulties and proposes a seq2seq-oriented decoding strategy called SR, which includes a new intermediate representation SSQL and a reranking method with score re-estimator to solve the above obstacles respectively. Experimental results demonstrate the effectiveness of our proposed techniques and T5-SR-3b achieves new state-of-the-art results on the Spider dataset.

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