Text-to-SQL semantic parsing is an important NLP task, which greatly
facilitates the interaction between users and the database and becomes the key
component in many human-computer interaction systems. Much recent progress in
text-to-SQL has been driven by large-scale datasets, but most of them are
centered on English. In this work, we present MultiSpider, the largest
multilingual text-to-SQL dataset which covers seven languages (English, German,
French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we
further identify the lexical and structural challenges of text-to-SQL (caused
by specific language properties and dialect sayings) and their intensity across
different languages. Experimental results under three typical settings
(zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in
accuracy in non-English languages. Qualitative and quantitative analyses are
conducted to understand the reason for the performance drop of each language.
Besides the dataset, we also propose a simple schema augmentation framework
SAVe (Schema-Augmentation-with-Verification), which significantly boosts the
overall performance by about 1.8% and closes the 29.5% performance gap across
languages.

本文介绍了 MultiSpider 数据集与 SAVe 框架，MultiSpider 数据集覆盖 7 种语言，且文中进一步提出了各种语言下，text-to-SQL 语义解析所面临的词汇和结构上的挑战，导致非英语言的解析准确率下降了 6.1％，而 SAVe 框架则通过对 Schema 进行增强从而有助于提升解析的性能。

MultiSpider：旨在基准测试多语言文本到 SQL 语义解析

MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic  Parsing

We introduce "Search with Amortized Value Estimates" (SAVE), an approach for
combining model-free Q-learning with model-based Monte-Carlo Tree Search
(MCTS). In SAVE, a learned prior over state-action values is used to guide
MCTS, which estimates an improved set of state-action values. The new
Q-estimates are then used in combination with real experience to update the
prior. This effectively amortizes the value computation performed by MCTS,
resulting in a cooperative relationship between model-free learning and
model-based search. SAVE can be implemented on top of any Q-learning agent with
access to a model, which we demonstrate by incorporating it into agents that
perform challenging physical reasoning tasks and Atari. SAVE consistently
achieves higher rewards with fewer training steps, and---in contrast to typical
model-based search approaches---yields strong performance with very small
search budgets. By combining real experience with information computed during
search, SAVE demonstrates that it is possible to improve on both the
performance of model-free learning and the computational cost of planning.

SAVE 是一种将模型无关的 Q - 学习与模型基于 Monte-Carlo 树搜索相结合的方法，它能够通过引导搜索来优化状态 - 动作值，从而在不增加计算成本的情况下提高学习性能，该方法已应用于物理推理任务和 Atari 游戏的智能体中展现出更好的性能。