English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Conversational text-to-SQL is designed to translate multi-turn naturallanguage questions into their corresponding SQL queries. Most state-of-the-artconversational text- to-SQL methods are incompatible with generativepre-trained language models (PLMs), such as T5. In this paper, we present atwo-stage unified MultI-task Generation frAmework (MIGA) that leverages PLMs'ability to tackle conversational text-to-SQL. In the pre-training stage, MIGAfirst decomposes the main task into several related sub-tasks and then unifiesthem into the same sequence-to-sequence (Seq2Seq) paradigm with task-specificnatural language prompts to boost the main task from multi-task training. Laterin the fine-tuning stage, we propose four SQL perturbations to alleviate theerror propagation problem. MIGA tends to achieve state-of-the-art performanceon two benchmarks (SparC and CoSQL). We also provide extensive analyses anddiscussions to shed light on some new perspectives for conversationaltext-to-SQL.

MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL