344 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
Improving Compositional Generalization with Latent Structure and Data Augmentation
Generic unstructured neural networks have been shown to struggle on
out-of-distribution compositional generalization. Compositional data
augmentation via example recombination has transferred some prior knowledge
about compositionality to such black-box neural models for several semantic
parsing tasks, but this often required task-specific engineering or provided
limited gains.
We present a more powerful data recombination method using a model called
Compositional Structure Learner (CSL). CSL is a generative model with a
quasi-synchronous context-free grammar backbone, which we induce from the
training data. We sample recombined examples from CSL and add them to the
fine-tuning data of a pre-trained sequence-to-sequence model (T5). This
procedure effectively transfers most of CSL's compositional bias to T5 for
diagnostic tasks, and results in a model even stronger than a T5-CSL ensemble
on two real world compositional generalization tasks. This results in new
state-of-the-art performance for these challenging semantic parsing tasks
requiring generalization to both natural language variation and novel
compositions of elements.Comment: NAACL 202
Learning words and syntactic cues in highly ambiguous contexts
The cross-situational word learning paradigm argues that word meanings can be approximated
by word-object associations, computed from co-occurrence statistics between
words and entities in the world. Lexicon acquisition involves simultaneously
guessing (1) which objects are being talked about (the ”meaning”) and (2) which words
relate to those objects. However, most modeling work focuses on acquiring meanings
for isolated words, largely neglecting relationships between words or physical entities,
which can play an important role in learning.
Semantic parsing, on the other hand, aims to learn a mapping between entire utterances
and compositional meaning representations where such relations are central.
The focus is the mapping between meaning and words, while utterance meanings are
treated as observed quantities.
Here, we extend the joint inference problem of word learning to account for compositional
meanings by incorporating a semantic parsing model for relating utterances
to non-linguistic context. Integrating semantic parsing and word learning permits us to
explore the impact of word-word and concept-concept relations.
The result is a joint-inference problem inherited from the word learning setting
where we must simultaneously learn utterance-level and individual word meanings,
only now we also contend with the many possible relationships between concepts in
the meaning and words in the sentence. To simplify design, we factorize the model into
separate modules, one for each of the world, the meaning, and the words, and merge
them into a single synchronous grammar for joint inference.
There are three main contributions. First, we introduce a novel word learning
model and accompanying semantic parser. Second, we produce a corpus which allows
us to demonstrate the importance of structure in word learning. Finally, we also
present a number of technical innovations required for implementing such a model
Compositional Semantic Parsing on Semi-Structured Tables
Two important aspects of semantic parsing for question answering are the
breadth of the knowledge source and the depth of logical compositionality.
While existing work trades off one aspect for another, this paper
simultaneously makes progress on both fronts through a new task: answering
complex questions on semi-structured tables using question-answer pairs as
supervision. The central challenge arises from two compounding factors: the
broader domain results in an open-ended set of relations, and the deeper
compositionality results in a combinatorial explosion in the space of logical
forms. We propose a logical-form driven parsing algorithm guided by strong
typing constraints and show that it obtains significant improvements over
natural baselines. For evaluation, we created a new dataset of 22,033 complex
questions on Wikipedia tables, which is made publicly available
Semantic Parsing with Dual Learning
Semantic parsing converts natural language queries into structured logical
forms. The paucity of annotated training samples is a fundamental challenge in
this field. In this work, we develop a semantic parsing framework with the dual
learning algorithm, which enables a semantic parser to make full use of data
(labeled and even unlabeled) through a dual-learning game. This game between a
primal model (semantic parsing) and a dual model (logical form to query) forces
them to regularize each other, and can achieve feedback signals from some
prior-knowledge. By utilizing the prior-knowledge of logical form structures,
we propose a novel reward signal at the surface and semantic levels which tends
to generate complete and reasonable logical forms. Experimental results show
that our approach achieves new state-of-the-art performance on ATIS dataset and
gets competitive performance on Overnight dataset.Comment: Accepted by ACL 2019 Long Pape
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