3 research outputs found
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context
Recently semantic parsing in context has received considerable attention,
which is challenging since there are complex contextual phenomena. Previous
works verified their proposed methods in limited scenarios, which motivates us
to conduct an exploratory study on context modeling methods under real-world
semantic parsing in context. We present a grammar-based decoding semantic
parser and adapt typical context modeling methods on top of it. We evaluate 13
context modeling methods on two large complex cross-domain datasets, and our
best model achieves state-of-the-art performances on both datasets with
significant improvements. Furthermore, we summarize the most frequent
contextual phenomena, with a fine-grained analysis on representative models,
which may shed light on potential research directions. Our code is available at
https://github.com/microsoft/ContextualSP.Comment: Accepted by IJCAI2020
(http://static.ijcai.org/2020-accepted_papers.html). SOLE copyright holder is
IJCAI (International Joint Conferences on Artificial Intelligence), all
rights reserve
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
Context-dependent text-to-SQL task has drawn much attention in recent years.
Previous models on context-dependent text-to-SQL task only concentrate on
utilizing historical user inputs. In this work, in addition to using encoders
to capture historical information of user inputs, we propose a database schema
interaction graph encoder to utilize historicalal information of database
schema items. In decoding phase, we introduce a gate mechanism to weigh the
importance of different vocabularies and then make the prediction of SQL
tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which
are two large complex context-dependent cross-domain text-to-SQL datasets. Our
model outperforms previous state-of-the-art model by a large margin and
achieves new state-of-the-art results on the two datasets. The comparison and
ablation results demonstrate the efficacy of our model and the usefulness of
the database schema interaction graph encoder.Comment: EMNLP 202
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL
technique allows users to query databases by using natural language questions.
Though significant progress in this area has been made recently, most parsers
may fall short when they are deployed in real systems. One main reason stems
from the difficulty of fully understanding the users' natural language
questions. In this paper, we include human in the loop and present a novel
parser-independent interactive approach (PIIA) that interacts with users using
multi-choice questions and can easily work with arbitrary parsers. Experiments
were conducted on two cross-domain datasets, the WikiSQL and the more complex
Spider, with five state-of-the-art parsers. These demonstrated that PIIA is
capable of enhancing the text-to-SQL performance with limited interaction turns
by using both simulation and human evaluation