133,752 research outputs found
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Traditional semantic parsers map language onto compositional, executable
queries in a fixed schema. This mapping allows them to effectively leverage the
information contained in large, formal knowledge bases (KBs, e.g., Freebase) to
answer questions, but it is also fundamentally limiting---these semantic
parsers can only assign meaning to language that falls within the KB's
manually-produced schema. Recently proposed methods for open vocabulary
semantic parsing overcome this limitation by learning execution models for
arbitrary language, essentially using a text corpus as a kind of knowledge
base. However, all prior approaches to open vocabulary semantic parsing replace
a formal KB with textual information, making no use of the KB in their models.
We show how to combine the disparate representations used by these two
approaches, presenting for the first time a semantic parser that (1) produces
compositional, executable representations of language, (2) can successfully
leverage the information contained in both a formal KB and a large corpus, and
(3) is not limited to the schema of the underlying KB. We demonstrate
significantly improved performance over state-of-the-art baselines on an
open-domain natural language question answering task.Comment: Re-written abstract and intro, other minor changes throughout. This
version published at AAAI 201
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
QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
Many natural language questions require recognizing and reasoning with
qualitative relationships (e.g., in science, economics, and medicine), but are
challenging to answer with corpus-based methods. Qualitative modeling provides
tools that support such reasoning, but the semantic parsing task of mapping
questions into those models has formidable challenges. We present QuaRel, a
dataset of diverse story questions involving qualitative relationships that
characterize these challenges, and techniques that begin to address them. The
dataset has 2771 questions relating 19 different types of quantities. For
example, "Jenny observes that the robot vacuum cleaner moves slower on the
living room carpet than on the bedroom carpet. Which carpet has more friction?"
We contribute (1) a simple and flexible conceptual framework for representing
these kinds of questions; (2) the QuaRel dataset, including logical forms,
exemplifying the parsing challenges; and (3) two novel models for this task,
built as extensions of type-constrained semantic parsing. The first of these
models (called QuaSP+) significantly outperforms off-the-shelf tools on QuaRel.
The second (QuaSP+Zero) demonstrates zero-shot capability, i.e., the ability to
handle new qualitative relationships without requiring additional training
data, something not possible with previous models. This work thus makes inroads
into answering complex, qualitative questions that require reasoning, and
scaling to new relationships at low cost. The dataset and models are available
at http://data.allenai.org/quarel.Comment: 9 pages, AAAI 201
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