user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles

Paper content:
We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory.
To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles.
We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query.
When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%.
Many mission-critical applications in health care, financial markets, and business process management store their information in relational databases BID10 BID22 BID16 .
Users access that information using a query language such as SQL.
Although expressive and powerful, SQL is difficult to master for non-technical users.
Even for an expert, writing SQL queries can be challenging as it requires knowing the exact schema of the database and the roles of various entities in the query.
Hence, a long-standing goal has been to allow users to interact with the database through natural language BID0 BID24 .The
key to achieving this goal is understanding the semantics of the natural language statements and mapping them to the intended SQL. This
problem, also known as NL2SQL, was previously understudied largely due to the availability of annotation. Without
paired natural language statement and SQL query, a weak supervision approach may be adopted which reduces supervision from annotated SQL queries to answers BID19 . This is
a more difficult learning problem. Therefore
only with recent release of a number of large-scale annotated NL2SQL datasets BID36 BID6 , we start to see a surge of interest in solving this problem.Existing NL2SQL approaches largely fall into two categories: sequence-to-sequence style neural "machine translation " systems BID36 BID5 and sets of modularized models with each predicting a specific part of the SQL queries BID32 BID34 . The former
class suffer from the requirement of labeling a single ground truth query while multiple semantically equivalent queries exist for each intent. For example
, as noticed by BID36 , the ordering of filtering conditions in a query does not affect execution but affects generation. To account
for this, techniques such as reinforcement learning have been used on top of those sequenceto-sequence models. The second
class of models employ a sequence-to-set approach: they first predict table columns present in the query and then independently predict the rest for each column. This avoids
the ordering issue, but makes it harder to leverage inter-dependencies among conditions.In this work, we develop a sequence-to-action parsing approach (Section 3) for the
NL2SQL problem. It incrementally
fills the slots of a SQL query with actions from an inventory designed for this task. Taking inspiration
from training oracles in incremental syntactic parsing BID8 , we further propose to use non-deterministic oracles (Section 4) for training the
incremental parsers. These oracles permit
multiple correct action continuations from a partial parse, thus are able to account for the logical form variations. Our model combines the
advantage of a sequence-to-sequence model that captures inter-dependencies within sequence of predictions and a SELECT`Height (ft)Ẁ HERE Name="Willis Tower" AND Location="Chicago" DISPLAYFORM0 What is the height of Willis Tower in Chicago?Figure 1: Our running example
. The input is a natural language
question and a table schema, and the output is an executable SQL query. Table contents are shown here,
but unknown to our models.modularized model that avoids any standarized linearization of the logical forms. We evaluate our models on the
WikiSQL dataset and observe a performance improvement of 2.1% when comparing non-deterministic oracles with traditional static oracles. We further combine our approach
and the execution-guided decoding strategy ) and achieve a new state-of-the-art performance with 87.1% test execution accuracy. Experiments on a filtered ATIS
dataset in addition confirm that our models can be applied to other NL2SQL datasets.
In this paper, we introduce a sequence-to-action incremental parsing approach for the NL2SQL task.
With the observation that multiple SQL queries can have the same or very similar semantics corresponding to a given natural language question, we propose to use non-deterministic oracles during training.
On the WikiSQL dataset, our model trained with the non-deterministic oracles achieves an execution accuracy of 83.7%, which is 2.3% higher than the current state of the art.
We also discuss using execution-guided decoding in combination with our model.
This leads to a further improvement of 3.4%, achieving a new state-of-the-art 87.1% execution accuracy on the test set.To the best of our knowledge, our work is the first to use non-deterministic oracles for training incremental semantic parsers.
Designing such non-deterministic oracles requires identification of multiple correct transition sequences for a given training instance, and an algorithm that decides the possible continuations for any intermediate state that will lead to one of the desired terminal states.
We have shown promising results for WikiSQL and filtered ATIS dataset and it would be interesting to extend our work to other more complex NL2SQL tasks and to other semantic parsing domains.

assistant: text: We design incremental sequence-to-action parsers for text-to-SQL task and achieve SOTA results. We further improve by using non-deterministic oracles to allow multiple correct action sequences.