266 research outputs found
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
To learn a semantic parser from denotations, a learning algorithm must search
over a combinatorially large space of logical forms for ones consistent with
the annotated denotations. We propose a new online learning algorithm that
searches faster as training progresses. The two key ideas are using macro
grammars to cache the abstract patterns of useful logical forms found thus far,
and holistic triggering to efficiently retrieve the most relevant patterns
based on sentence similarity. On the WikiTableQuestions dataset, we first
expand the search space of an existing model to improve the state-of-the-art
accuracy from 38.7% to 42.7%, and then use macro grammars and holistic
triggering to achieve an 11x speedup and an accuracy of 43.7%.Comment: EMNLP 201
AMR Dependency Parsing with a Typed Semantic Algebra
We present a semantic parser for Abstract Meaning Representations which
learns to parse strings into tree representations of the compositional
structure of an AMR graph. This allows us to use standard neural techniques for
supertagging and dependency tree parsing, constrained by a linguistically
principled type system. We present two approximative decoding algorithms, which
achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see
https://acl2018.org/programme/papers/
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
Many algorithms for Knowledge-Based Question Answering (KBQA) depend on
semantic parsing, which translates a question to its logical form. When only
weak supervision is provided, it is usually necessary to search valid logical
forms for model training. However, a complex question typically involves a huge
search space, which creates two main problems: 1) the solutions limited by
computation time and memory usually reduce the success rate of the search, and
2) spurious logical forms in the search results degrade the quality of training
data. These two problems lead to a poorly-trained semantic parsing model. In
this work, we propose an effective search method for weakly supervised KBQA
based on operator prediction for questions. With search space constrained by
predicted operators, sufficient search paths can be explored, more valid
logical forms can be derived, and operators possibly causing spurious logical
forms can be avoided. As a result, a larger proportion of questions in a weakly
supervised training set are equipped with logical forms, and fewer spurious
logical forms are generated. Such high-quality training data directly
contributes to a better semantic parsing model. Experimental results on one of
the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our
approach: improving the precision from 67% to 72% and the recall from 67% to
72% in terms of the overall score
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
Leveraging Language to Learn Program Abstractions and Search Heuristics
Inductive program synthesis, or inferring programs from examples of desired
behavior, offers a general paradigm for building interpretable, robust, and
generalizable machine learning systems. Effective program synthesis depends on
two key ingredients: a strong library of functions from which to build
programs, and an efficient search strategy for finding programs that solve a
given task. We introduce LAPS (Language for Abstraction and Program Search), a
technique for using natural language annotations to guide joint learning of
libraries and neurally-guided search models for synthesis. When integrated into
a state-of-the-art library learning system (DreamCoder), LAPS produces
higher-quality libraries and improves search efficiency and generalization on
three domains -- string editing, image composition, and abstract reasoning
about scenes -- even when no natural language hints are available at test time.Comment: appeared in Thirty-eighth International Conference on Machine
Learning (ICML 2021
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