Text-to-SQL parsers typically struggle with databases unseen during the train
time. Adapting parsers to new databases is a challenging problem due to the
lack of natural language queries in the new schemas. We present ReFill, a
framework for synthesizing high-quality and textually diverse parallel datasets
for adapting a Text-to-SQL parser to a target schema. ReFill learns to
retrieve-and-edit text queries from the existing schemas and transfers them to
the target schema. We show that retrieving diverse existing text, masking their
schema-specific tokens, and refilling with tokens relevant to the target
schema, leads to significantly more diverse text queries than achievable by
standard SQL-to-Text generation methods. Through experiments spanning multiple
databases, we demonstrate that fine-tuning parsers on datasets synthesized
using ReFill consistently outperforms the prior data-augmentation methods.

研究人员通过构建高质量、多样化的数据集来改进 Text-to-SQL 解析器的适配性和性能。实验表明使用 ReFill 构建的数据集，可以显著提高解析器适应新数据库的效果。

面向文本到 SQL 解析器的跨数据库适应的多样化并行数据合成

Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers

Image completion is a task that aims to fill in the missing region of a
masked image with plausible contents.However, existing image completion methods
tend to fill in the missing region with the surrounding texture instead of
hallucinating a visual instance that is suitable in accordance with the context
of the scene. In this work, we propose a novel image completion model, dubbed
Refill, that hallucinates the missing instance that harmonizes well with - and
thus preserves - the original context. Refill first adopts a transformer
architecture that considers the types, locations of the visible instances, and
the location of the missing region. Then, Refill completes the missing
foreground and background semantic segmentation masks within the missing
region, providing pixel-level semantic and structural guidance to generate
missing contents with seamless boundaries. Finally, we condition the image
synthesis blocks of Refill using the completed segmentation mask to generate
photo-realistic contents to fill out the missing region. Experimental results
show the superiority of Refill over state-of-the-art image completion
approaches on various natural images.

本文提出了一种名为 Refill 的基于 Transformer 网络和前景背景语义分割的图像修复模型，通过对原始场景上下文的考虑，生成高质量的缺失部分内容。实验证明该模型优于现有的 state-of-the-art 方法。