44,980 research outputs found
Learning the Semantics of Manipulation Action
In this paper we present a formal computational framework for modeling
manipulation actions. The introduced formalism leads to semantics of
manipulation action and has applications to both observing and understanding
human manipulation actions as well as executing them with a robotic mechanism
(e.g. a humanoid robot). It is based on a Combinatory Categorial Grammar. The
goal of the introduced framework is to: (1) represent manipulation actions with
both syntax and semantic parts, where the semantic part employs
-calculus; (2) enable a probabilistic semantic parsing schema to learn
the -calculus representation of manipulation action from an annotated
action corpus of videos; (3) use (1) and (2) to develop a system that visually
observes manipulation actions and understands their meaning while it can reason
beyond observations using propositional logic and axiom schemata. The
experiments conducted on a public available large manipulation action dataset
validate the theoretical framework and our implementation
Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena
Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis;
and it contains many sorts of disfluency such as mid-utterance/sentence
hesitations, interruptions, and self-corrections. But training data for machine
learning approaches to dialogue processing is often either cleaned-up or wholly
synthetic in order to avoid such phenomena. The question then arises of how
well systems trained on such clean data generalise to real spontaneous
dialogue, or indeed whether they are trainable at all on naturally occurring
dialogue data. To answer this question, we created a new corpus called bAbI+ by
systematically adding natural spontaneous incremental dialogue phenomena such
as restarts and self-corrections to the Facebook AI Research's bAbI dialogues
dataset. We then explore the performance of a state-of-the-art retrieval model,
MemN2N, on this more natural dataset. Results show that the semantic accuracy
of the MemN2N model drops drastically; and that although it is in principle
able to learn to process the constructions in bAbI+, it needs an impractical
amount of training data to do so. Finally, we go on to show that an
incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both
bAbI and bAbI+, highlighting the generalisation properties of linguistically
informed dialogue models.Comment: 9 pages, 3 figures, 2 tables. Accepted as a full paper for SemDial
201
Synthesizing Program Input Grammars
We present an algorithm for synthesizing a context-free grammar encoding the
language of valid program inputs from a set of input examples and blackbox
access to the program. Our algorithm addresses shortcomings of existing grammar
inference algorithms, which both severely overgeneralize and are prohibitively
slow. Our implementation, GLADE, leverages the grammar synthesized by our
algorithm to fuzz test programs with structured inputs. We show that GLADE
substantially increases the incremental coverage on valid inputs compared to
two baseline fuzzers
Learning Tractable Probabilistic Models for Fault Localization
In recent years, several probabilistic techniques have been applied to
various debugging problems. However, most existing probabilistic debugging
systems use relatively simple statistical models, and fail to generalize across
multiple programs. In this work, we propose Tractable Fault Localization Models
(TFLMs) that can be learned from data, and probabilistically infer the location
of the bug. While most previous statistical debugging methods generalize over
many executions of a single program, TFLMs are trained on a corpus of
previously seen buggy programs, and learn to identify recurring patterns of
bugs. Widely-used fault localization techniques such as TARANTULA evaluate the
suspiciousness of each line in isolation; in contrast, a TFLM defines a joint
probability distribution over buggy indicator variables for each line. Joint
distributions with rich dependency structure are often computationally
intractable; TFLMs avoid this by exploiting recent developments in tractable
probabilistic models (specifically, Relational SPNs). Further, TFLMs can
incorporate additional sources of information, including coverage-based
features such as TARANTULA. We evaluate the fault localization performance of
TFLMs that include TARANTULA scores as features in the probabilistic model. Our
study shows that the learned TFLMs isolate bugs more effectively than previous
statistical methods or using TARANTULA directly.Comment: Fifth International Workshop on Statistical Relational AI (StaR-AI
2015
- …