Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (1909.00786v2)

Abstract: We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

The paper proposes an innovative approach to generating SQL queries from natural language in multi-turn interactions, enhancing the ability of systems to handle context-dependent queries across various domains effectively. The focus is on improving SQL generation quality by leveraging interaction history through an editing mechanism rather than generating from scratch or relying solely on copy mechanisms.

Overview

The core observation driving the research is the linguistic and structural overlap often present between consecutively asked questions and their corresponding SQL queries. Building on this insight, the authors suggest a method to refine the generation process by treating SQL queries as sequences and utilizing token-level data from previously predicted queries. This approach allows precise modifications to individual tokens, offering robustness against error propagation and providing a flexible mechanism for query refinement.

The methodology features a combined encoder-decoder architecture, integrating a novel utterance-table encoder with co-attention mechanisms and a tailored table-aware decoder. This framework ensures that both user utterances and complex table schemas are effectively encoded and leveraged during SQL query generation.

Experimental Results

Using the SParC dataset, the research demonstrates substantial improvements over existing baselines, marking advancements in the question and interaction match accuracy metrics. Notably, their model exceeds the purely generative approaches by a significant margin when employing their editing mechanism, which accommodates token-level adjustments based on preceding query sequences. The results reveal that by reusing prior SQL query information, accuracy improvement is attainable in both question match (up by 7%) and interaction match (up by 11%) compared to the state-of-the-art benchmarks. Additionally, the utilization of BERT embeddings further enhances performance, emphasizing the integration of contextualized embeddings in SQL generation tasks.

Implications and Future Directions

The implications of this research are sizable, notably in fields that require accurate and context-aware system responses from databases, such as customer support systems and data analysis tools. The robustness of the technique to error propagation and its ability to selectively reuse relevant query parts is particularly valuable in improving real-time system interactions.

The paper invites further exploration into the dynamic interplay between natural language understanding and structured query formulations. Additionally, the creation of more extensive cross-domain semantic parsing datasets could further refine these approaches, encouraging the implementation of complex, real-world systems. Future research could also focus on refining the mechanisms to incorporate user utterance ambiguities and clarifications, thus mimicking more realistic conversational scenarios in database querying tasks.

In conclusion, the paper presents an effective approach to SQL query generation by editing, an area that holds promise for advancing natural language understanding and processing tasks.