Natural language to SQL (NL2SQL) aims to parse a natural language with a
given database into a SQL query, which widely appears in practical Internet
applications. Jointly encode database schema and question utterance is a
difficult but important task in NL2SQL. One solution is to treat the input as a
heterogeneous graph. However, it failed to learn good word representation in
question utterance. Learning better word representation is important for
constructing a well-designed NL2SQL system. To solve the challenging task, we
present a Relation aware Semi-autogressive Semantic Parsing (\MODN) ~framework,
which is more adaptable for NL2SQL. It first learns relation embedding over the
schema entities and question words with predefined schema relations with
ELECTRA and relation aware transformer layer as backbone. Then we decode the
query SQL with a semi-autoregressive parser and predefined SQL syntax. From
empirical results and case study, our model shows its effectiveness in learning
better word representation in NL2SQL.

该论文提出了一种基于关系嵌入的半自回归语义分析 NL2SQL 方法，通过 ELECTRA 和关系感知 transformer 层实现与模式实体及问题单词之间的关系嵌入，再使用半自回归解析器和预定义的 SQL 语法解码查询 SQL，从实验结果和案例研究来看，该方法在 NL2SQL 中具有更好的词汇表现力。

NL2SQL 的关系感知半自回归语义解析

Relation Aware Semi-autoregressive Semantic Parsing for NL2SQL

Point-of-Interest (POI) recommendation is one of the most important
location-based services helping people discover interesting venues or services.
However, the extreme user-POI matrix sparsity and the varying spatio-temporal
context pose challenges for POI systems, which affects the quality of POI
recommendations. To this end, we propose a translation-based relation embedding
for POI recommendation. Our approach encodes the temporal and geographic
information, as well as semantic contents effectively in a low-dimensional
relation space by using Knowledge Graph Embedding techniques. To further
alleviate the issue of user-POI matrix sparsity, a combined matrix
factorization framework is built on a user-POI graph to enhance the inference
of dynamic personal interests by exploiting the side-information. Experiments
on two real-world datasets demonstrate the effectiveness of our proposed model.

研究了 POI（点 - of-interest）推荐系统中用户 - POI 数据稀疏性、时空上下文变化以及语义信息对推荐质量的影响，提出了一种基于转换关系嵌入和知识图谱嵌入技术的 POI 推荐模型并构建了基于用户 - POI 图的组合矩阵分解框架来增强动态个人兴趣的推理。实验证明该方法具有较好的推荐效果。