314 research outputs found
A Syntactic Neural Model for General-Purpose Code Generation
We consider the problem of parsing natural language descriptions into source
code written in a general-purpose programming language like Python. Existing
data-driven methods treat this problem as a language generation task without
considering the underlying syntax of the target programming language. Informed
by previous work in semantic parsing, in this paper we propose a novel neural
architecture powered by a grammar model to explicitly capture the target syntax
as prior knowledge. Experiments find this an effective way to scale up to
generation of complex programs from natural language descriptions, achieving
state-of-the-art results that well outperform previous code generation and
semantic parsing approaches.Comment: To appear in ACL 201
On-the-fly Table Generation
Many information needs revolve around entities, which would be better
answered by summarizing results in a tabular format, rather than presenting
them as a ranked list. Unlike previous work, which is limited to retrieving
existing tables, we aim to answer queries by automatically compiling a table in
response to a query. We introduce and address the task of on-the-fly table
generation: given a query, generate a relational table that contains relevant
entities (as rows) along with their key properties (as columns). This problem
is decomposed into three specific subtasks: (i) core column entity ranking,
(ii) schema determination, and (iii) value lookup. We employ a feature-based
approach for entity ranking and schema determination, combining deep semantic
features with task-specific signals. We further show that these two subtasks
are not independent of each other and can assist each other in an iterative
manner. For value lookup, we combine information from existing tables and a
knowledge base. Using two sets of entity-oriented queries, we evaluate our
approach both on the component level and on the end-to-end table generation
task.Comment: The 41st International ACM SIGIR Conference on Research and
Development in Information Retrieva
Merging Weak and Active Supervision for Semantic Parsing
A semantic parser maps natural language commands (NLs) from the users to
executable meaning representations (MRs), which are later executed in certain
environment to obtain user-desired results. The fully-supervised training of
such parser requires NL/MR pairs, annotated by domain experts, which makes them
expensive to collect. However, weakly-supervised semantic parsers are learnt
only from pairs of NL and expected execution results, leaving the MRs latent.
While weak supervision is cheaper to acquire, learning from this input poses
difficulties. It demands that parsers search a large space with a very weak
learning signal and it is hard to avoid spurious MRs that achieve the correct
answer in the wrong way. These factors lead to a performance gap between
parsers trained in weakly- and fully-supervised setting. To bridge this gap, we
examine the intersection between weak supervision and active learning, which
allows the learner to actively select examples and query for manual annotations
as extra supervision to improve the model trained under weak supervision. We
study different active learning heuristics for selecting examples to query, and
various forms of extra supervision for such queries. We evaluate the
effectiveness of our method on two different datasets. Experiments on the
WikiSQL show that by annotating only 1.8% of examples, we improve over a
state-of-the-art weakly-supervised baseline by 6.4%, achieving an accuracy of
79.0%, which is only 1.3% away from the model trained with full supervision.
Experiments on WikiTableQuestions with human annotators show that our method
can improve the performance with only 100 active queries, especially for
weakly-supervised parsers learnt from a cold start.Comment: AAAI 2020 Main Track [Oral] (To appear
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