3,468 research outputs found
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Ten virtues of structured graphs
This paper extends the invited talk by the first author about the virtues
of structured graphs. The motivation behind the talk and this paper relies on our
experience on the development of ADR, a formal approach for the design of styleconformant,
reconfigurable software systems. ADR is based on hierarchical graphs
with interfaces and it has been conceived in the attempt of reconciling software architectures
and process calculi by means of graphical methods. We have tried to
write an ADR agnostic paper where we raise some drawbacks of flat, unstructured
graphs for the design and analysis of software systems and we argue that hierarchical,
structured graphs can alleviate such drawbacks
Naturalālanguage processing applied to an ITS interface
The aim of this paper is to show that with a subset of a natural language, simple systems running on PCs can be developed that can nevertheless be an effective tool for interfacing purposes in the building of an Intelligent Tutoring System (ITS). After presenting the special characteristics of the Smalltalk/V language, which provides an appropriate environment for the development of an interface, the overall architecture of the interface module is discussed. We then show how sentences are parsed by the interface, and how interaction takes place with the user. The knowledgeāacquisition phase is subsequently described. Finally, some excerpts from a tutoring session concerned with elementary geometry are discussed, and some of the problems and limitations of the approach are illustrated
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
Concurrent Lexicalized Dependency Parsing: The ParseTalk Model
A grammar model for concurrent, object-oriented natural language parsing is
introduced. Complete lexical distribution of grammatical knowledge is achieved
building upon the head-oriented notions of valency and dependency, while
inheritance mechanisms are used to capture lexical generalizations. The
underlying concurrent computation model relies upon the actor paradigm. We
consider message passing protocols for establishing dependency relations and
ambiguity handling.Comment: 90kB, 7pages Postscrip
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
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