5,398 research outputs found
Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Translating natural language to SQL queries for table-based question
answering is a challenging problem and has received significant attention from
the research community. In this work, we extend a pointer-generator and
investigate the order-matters problem in semantic parsing for SQL. Even though
our model is a straightforward extension of a general-purpose
pointer-generator, it outperforms early works for WikiSQL and remains
competitive to concurrently introduced, more complex models. Moreover, we
provide a deeper investigation of the potential order-matters problem that
could arise due to having multiple correct decoding paths, and investigate the
use of REINFORCE as well as a dynamic oracle in this context
Question Generation from SQL Queries Improves Neural Semantic Parsing
We study how to learn a semantic parser of state-of-the-art accuracy with
less supervised training data. We conduct our study on WikiSQL, the largest
hand-annotated semantic parsing dataset to date. First, we demonstrate that
question generation is an effective method that empowers us to learn a
state-of-the-art neural network based semantic parser with thirty percent of
the supervised training data. Second, we show that applying question generation
to the full supervised training data further improves the state-of-the-art
model. In addition, we observe that there is a logarithmic relationship between
the accuracy of a semantic parser and the amount of training data.Comment: The paper will be presented in EMNLP 201
Weakly-supervised Semantic Parsing with Abstract Examples
Training semantic parsers from weak supervision (denotations) rather than
strong supervision (programs) complicates training in two ways. First, a large
search space of potential programs needs to be explored at training time to
find a correct program. Second, spurious programs that accidentally lead to a
correct denotation add noise to training. In this work we propose that in
closed worlds with clear semantic types, one can substantially alleviate these
problems by utilizing an abstract representation, where tokens in both the
language utterance and program are lifted to an abstract form. We show that
these abstractions can be defined with a handful of lexical rules and that they
result in sharing between different examples that alleviates the difficulties
in training. To test our approach, we develop the first semantic parser for
CNLVR, a challenging visual reasoning dataset, where the search space is large
and overcoming spuriousness is critical, because denotations are either TRUE or
FALSE, and thus random programs are likely to lead to a correct denotation. Our
method substantially improves performance, and reaches 82.5% accuracy, a 14.7%
absolute accuracy improvement compared to the best reported accuracy so far.Comment: CNLVR,NLVR. Accepted to ACL 201
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Research on parsing language to SQL has largely ignored the structure of the
database (DB) schema, either because the DB was very simple, or because it was
observed at both training and test time. In Spider, a recently-released
text-to-SQL dataset, new and complex DBs are given at test time, and so the
structure of the DB schema can inform the predicted SQL query. In this paper,
we present an encoder-decoder semantic parser, where the structure of the DB
schema is encoded with a graph neural network, and this representation is later
used at both encoding and decoding time. Evaluation shows that encoding the
schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically
above the current state of the art, which is at 19.7%.Comment: Accepted as a short paper at ACL 201
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Conversational semantic parsing over tables requires knowledge acquiring and
reasoning abilities, which have not been well explored by current
state-of-the-art approaches. Motivated by this fact, we propose a
knowledge-aware semantic parser to improve parsing performance by integrating
various types of knowledge. In this paper, we consider three types of
knowledge, including grammar knowledge, expert knowledge, and external resource
knowledge. First, grammar knowledge empowers the model to effectively replicate
previously generated logical form, which effectively handles the co-reference
and ellipsis phenomena in conversation Second, based on expert knowledge, we
propose a decomposable model, which is more controllable compared with
traditional end-to-end models that put all the burdens of learning on
trial-and-error in an end-to-end way. Third, external resource knowledge, i.e.,
provided by a pre-trained language model or an entity typing model, is used to
improve the representation of question and table for a better semantic
understanding. We conduct experiments on the SequentialQA dataset. Results show
that our knowledge-aware model outperforms the state-of-the-art approaches.
Incremental experimental results also prove the usefulness of various
knowledge. Further analysis shows that our approach has the ability to derive
the meaning representation of a context-dependent utterance by leveraging
previously generated outcomes
Generating Logical Forms from Graph Representations of Text and Entities
Structured information about entities is critical for many semantic parsing
tasks. We present an approach that uses a Graph Neural Network (GNN)
architecture to incorporate information about relevant entities and their
relations during parsing. Combined with a decoder copy mechanism, this approach
provides a conceptually simple mechanism to generate logical forms with
entities. We demonstrate that this approach is competitive with the
state-of-the-art across several tasks without pre-training, and outperforms
existing approaches when combined with BERT pre-training.Comment: ACL 201
Semantic Parsing with Syntax- and Table-Aware SQL Generation
We present a generative model to map natural language questions into SQL
queries. Existing neural network based approaches typically generate a SQL
query word-by-word, however, a large portion of the generated results are
incorrect or not executable due to the mismatch between question words and
table contents. Our approach addresses this problem by considering the
structure of table and the syntax of SQL language. The quality of the generated
SQL query is significantly improved through (1) learning to replicate content
from column names, cells or SQL keywords; and (2) improving the generation of
WHERE clause by leveraging the column-cell relation. Experiments are conducted
on WikiSQL, a recently released dataset with the largest question-SQL pairs.
Our approach significantly improves the state-of-the-art execution accuracy
from 69.0% to 74.4%
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions
We describe two new related resources that facilitate modelling of general
knowledge reasoning in 4th grade science exams. The first is a collection of
curated facts in the form of tables, and the second is a large set of
crowd-sourced multiple-choice questions covering the facts in the tables.
Through the setup of the crowd-sourced annotation task we obtain implicit
alignment information between questions and tables. We envisage that the
resources will be useful not only to researchers working on question answering,
but also to people investigating a diverse range of other applications such as
information extraction, question parsing, answer type identification, and
lexical semantic modelling.Comment: Keywords: Data, General Knowledge, Tables, Question Answering, MCQ,
Crowd-sourcing, Mechanical Tur
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
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%
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