29 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
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language
interfaces to databases for new domains, whose performance improves over time
based on user feedback, and requires minimal intervention. To achieve this, we
adapt neural sequence models to map utterances directly to SQL with its full
expressivity, bypassing any intermediate meaning representations. These models
are immediately deployed online to solicit feedback from real users to flag
incorrect queries. Finally, the popularity of SQL facilitates gathering
annotations for incorrect predictions using the crowd, which is directly used
to improve our models. This complete feedback loop, without intermediate
representations or database specific engineering, opens up new ways of building
high quality semantic parsers. Experiments suggest that this approach can be
deployed quickly for any new target domain, as we show by learning a semantic
parser for an online academic database from scratch.Comment: Accepted at ACL 201
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
In a real-world dialogue system, generated responses must satisfy several
interlocking constraints: being informative, truthful, and easy to control. The
two predominant paradigms in language generation -- neural language modeling
and rule-based generation -- both struggle to satisfy these constraints. Even
the best neural models are prone to hallucination and omission of information,
while existing formalisms for rule-based generation make it difficult to write
grammars that are both flexible and fluent. We describe a hybrid architecture
for dialogue response generation that combines the strengths of both
approaches. This architecture has two components. First, a rule-based content
selection model defined using a new formal framework called dataflow
transduction, which uses declarative rules to transduce a dialogue agent's
computations (represented as dataflow graphs) into context-free grammars
representing the space of contextually acceptable responses. Second, a
constrained decoding procedure that uses these grammars to constrain the output
of a neural language model, which selects fluent utterances. The resulting
system outperforms both rule-based and learned approaches in human evaluations
of fluency, relevance, and truthfulness
Learning an Executable Neural Semantic Parser
This paper describes a neural semantic parser that maps natural language
utterances onto logical forms which can be executed against a task-specific
environment, such as a knowledge base or a database, to produce a response. The
parser generates tree-structured logical forms with a transition-based approach
which combines a generic tree-generation algorithm with domain-general
operations defined by the logical language. The generation process is modeled
by structured recurrent neural networks, which provide a rich encoding of the
sentential context and generation history for making predictions. To tackle
mismatches between natural language and logical form tokens, various attention
mechanisms are explored. Finally, we consider different training settings for
the neural semantic parser, including a fully supervised training where
annotated logical forms are given, weakly-supervised training where denotations
are provided, and distant supervision where only unlabeled sentences and a
knowledge base are available. Experiments across a wide range of datasets
demonstrate the effectiveness of our parser.Comment: In Journal of Computational Linguistic
Finding analogies using the singular value decomposition
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 59-61).We present CROSSBRIDGE, an algorithm for finding analogies in large, sparse semantic networks. We treat analogies as comparisons between domains of knowledge. A domain is a small semantic network, i.e., a set of concepts and binary relations between concepts. We treat our knowledge base (the large semantic network) as if it contained many domains of knowledge, then apply dimensionality reduction to find the most salient relation structures among the domains. Relation structures are systems of relations similar to the structures mapped between domains in structure mapping[6]. These structures are effectively n-ary relations formed by combining multiple pairwise relations. The most salient relation structures form the basis of domain space, a space containing all domains of knowledge from the large semantic network. The construction of domain space places analogous domains near each other in domain space. CROSSBRIDGE finds analogies using similarity information from domain space and a heuristic search process. We evaluate our method on ConceptNet[10], a large semantic network of common sense knowledge. We compare our approach with an implementation of structure mapping and show that our algorithm is more efficient and has superior analogy recall.by Jayant Krishnamurthy.M.Eng