Similar Questions Correspond to Similar SQL Queries: A Case-Based Reasoning Approach for Text-to-SQL Translation

Yu, Wei; Guo, Xiaoting; Chen, Fei; Chang, Tao; Wang, Mengzhu; Xiao-dong, Wang

doi:10.1007/978-3-030-86957-1_20

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…When the knowledge source of CBR is in a text form, the method used is called text-based case-based reasoning. Yu et al (2021) applied the CBR method to the Text-to-SQL task in semantic parsing, following the classical CBR process, to implement the translation process from unstructured natural language to structured SQL. In contrast, Upadhyay et al (2021) proposed a CBR method to generate text from data.…”

Section: Other Applicationsmentioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence. In this article, the development of CBR is mainly reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were formed in solving problems by CBR are summarized. Moreover, the application fields of CBR are sorted. Finally, according to the idea of deep learning and interpretable artificial intelligence, the main challenges for the future development of CBR are proposed.

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Section: Other Applicationsmentioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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“…Beyond the legal domain, the idea of exploiting similar cases has recently been used for question answering [51], semantic parsing [72], text generation [64] and language modelling [31] among many others. Within the biomedical domain, one relevant line of research aims to capture the similarity between different patients to predict, for example, a diagnosis or treatment [24,26,47], usually by learning a dense vector representation of each patient.…”

Section: Related Workmentioning

confidence: 99%

Interpreting Patient Descriptions using Distantly Supervised Similar Case Retrieval

Alghanmi

Espinosa-Anke

Schockaert

2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Biomedical natural language processing often involves the interpretation of patient descriptions, for instance for diagnosis or for recommending treatments. Current methods, based on biomedical language models, have been found to struggle with such tasks. Moreover, retrieval augmented strategies have only had limited success, as it is rare to find sentences which express the exact type of knowledge that is needed for interpreting a given patient description. For this reason, rather than attempting to retrieve explicit medical knowledge, we instead propose to rely on a nearest neighbour strategy. First, we retrieve text passages that are similar to the given patient description, and are thus likely to describe patients in similar situations, while also mentioning some hypothesis (e.g. a possible diagnosis of the patient). We then judge the likelihood of the hypothesis based on the similarity of the retrieved passages. Identifying similar cases is challenging, however, as descriptions of similar patients may superficially look rather different, among others because they often contain an abundance of irrelevant details. To address this challenge, we propose a strategy that relies on a distantly supervised cross-encoder. Despite its conceptual simplicity, we find this strategy to be effective in practice. CCS CONCEPTS• Applied computing → Life and medical sciences; • Information systems → Information retrieval; • Computing methodologies → Natural language processing.

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