318 research outputs found
Lambda Dependency-Based Compositional Semantics
This short note presents a new formal language, lambda dependency-based
compositional semantics (lambda DCS) for representing logical forms in semantic
parsing. By eliminating variables and making existential quantification
implicit, lambda DCS logical forms are generally more compact than those in
lambda calculus
Reified Context Models
A classic tension exists between exact inference in a simple model and
approximate inference in a complex model. The latter offers expressivity and
thus accuracy, but the former provides coverage of the space, an important
property for confidence estimation and learning with indirect supervision. In
this work, we introduce a new approach, reified context models, to reconcile
this tension. Specifically, we let the amount of context (the arity of the
factors in a graphical model) be chosen "at run-time" by reifying it---that is,
letting this choice itself be a random variable inside the model. Empirically,
we show that our approach obtains expressivity and coverage on three natural
language tasks
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
How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions
How much is 131 million US dollars? To help readers put such numbers in
context, we propose a new task of automatically generating short descriptions
known as perspectives, e.g. "$131 million is about the cost to employ everyone
in Texas over a lunch period". First, we collect a dataset of numeric mentions
in news articles, where each mention is labeled with a set of rated
perspectives. We then propose a system to generate these descriptions
consisting of two steps: formula construction and description generation. In
construction, we compose formulae from numeric facts in a knowledge base and
rank the resulting formulas based on familiarity, numeric proximity and
semantic compatibility. In generation, we convert a formula into natural
language using a sequence-to-sequence recurrent neural network. Our system
obtains a 15.2% F1 improvement over a non-compositional baseline at formula
construction and a 12.5 BLEU point improvement over a baseline description
generation
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
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