Conversational, multi-turn, text-to-SQL (CoSQL) tasks map natural language
utterances in a dialogue to SQL queries. State-of-the-art (SOTA) systems use
large, pre-trained and finetuned language models, such as the T5-family, in
conjunction with constrained decoding. With multi-tasking (MT) over coherent
tasks with discrete prompts during training, we improve over specialized
text-to-SQL T5-family models. Based on Oracle analyses over n-best hypotheses,
we apply a query plan model and a schema linking algorithm as rerankers.
Combining MT and reranking, our results using T5-3B show absolute accuracy
improvements of 1.0% in exact match and 3.4% in execution match over a SOTA
baseline on CoSQL. While these gains consistently manifest at turn level,
context dependent turns are considerably harder. We conduct studies to tease
apart errors attributable to domain and compositional generalization, with the
latter remaining a challenge for multi-turn conversations, especially in
generating SQL with unseen parse trees.

通过多任务联合训练和模式重排应用于 text-to-SQL 任务中，可以显著提高模型的表现，进一步结合查询计划模型和模式链接算法后，T5-3B 的精确匹配和执行匹配的准确度均超过 SOTA 基线。

面向会话的文本到 SQL 转换：现状和未来挑战的探索

Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead

The task of multi-turn text-to-SQL semantic parsing aims to translate natural
language utterances in an interaction into SQL queries in order to answer them
using a database which normally contains multiple table schemas. Previous
studies on this task usually utilized contextual information to enrich
utterance representations and to further influence the decoding process. While
they ignored to describe and track the interaction states which are determined
by history SQL queries and are related with the intent of current utterance. In
this paper, two kinds of interaction states are defined based on schema items
and SQL keywords separately. A relational graph neural network and a non-linear
layer are designed to update the representations of these two states
respectively. The dynamic schema-state and SQL-state representations are then
utilized to decode the SQL query corresponding to current utterance.
Experimental results on the challenging CoSQL dataset demonstrate the
effectiveness of our proposed method, which achieves better performance than
other published methods on the task leaderboard.

本文提出一种基于关系图神经网络的文本转 SQL 语义解析模型，通过定义基于数据库模式项和关键词的交互状态并设计相应的更新机制，以实现更好的解析效果，在 CoSQL 数据集上表现优异。

多轮文本到 SQL 语义解析的交互状态跟踪

Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing

We present CoSQL, a corpus for building cross-domain, general-purpose
database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+
annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k
dialogues querying 200 complex DBs spanning 138 domains. Each dialogue
simulates a real-world DB query scenario with a crowd worker as a user
exploring the DB and a SQL expert retrieving answers with SQL, clarifying
ambiguous questions, or otherwise informing of unanswerable questions. When
user questions are answerable by SQL, the expert describes the SQL and
execution results to the user, hence maintaining a natural interaction flow.
CoSQL introduces new challenges compared to existing task-oriented dialogue
datasets:(1) the dialogue states are grounded in SQL, a domain-independent
executable representation, instead of domain-specific slot-value pairs, and (2)
because testing is done on unseen databases, success requires generalizing to
new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking,
response generation from query results, and user dialogue act prediction. We
evaluate a set of strong baselines for each task and show that CoSQL presents
significant challenges for future research. The dataset, baselines, and
leaderboard will be released at this https URL

CoSQL 是一个用于构建跨领域、通用的数据库查询对话系统的语料库，其对话状态以 SQL 作为域无关的可执行表示，提供了一种维护自然交互流的方法。