24,765 research outputs found
Modelling Users, Intentions, and Structure in Spoken Dialog
We outline how utterances in dialogs can be interpreted using a partial first
order logic. We exploit the capability of this logic to talk about the truth
status of formulae to define a notion of coherence between utterances and
explain how this coherence relation can serve for the construction of AND/OR
trees that represent the segmentation of the dialog. In a BDI model we
formalize basic assumptions about dialog and cooperative behaviour of
participants. These assumptions provide a basis for inferring speech acts from
coherence relations between utterances and attitudes of dialog participants.
Speech acts prove to be useful for determining dialog segments defined on the
notion of completing expectations of dialog participants. Finally, we sketch
how explicit segmentation signalled by cue phrases and performatives is covered
by our dialog model.Comment: 17 page
Object-Oriented Dynamics Learning through Multi-Level Abstraction
Object-based approaches for learning action-conditioned dynamics has
demonstrated promise for generalization and interpretability. However, existing
approaches suffer from structural limitations and optimization difficulties for
common environments with multiple dynamic objects. In this paper, we present a
novel self-supervised learning framework, called Multi-level Abstraction
Object-oriented Predictor (MAOP), which employs a three-level learning
architecture that enables efficient object-based dynamics learning from raw
visual observations. We also design a spatial-temporal relational reasoning
mechanism for MAOP to support instance-level dynamics learning and handle
partial observability. Our results show that MAOP significantly outperforms
previous methods in terms of sample efficiency and generalization over novel
environments for learning environment models. We also demonstrate that learned
dynamics models enable efficient planning in unseen environments, comparable to
true environment models. In addition, MAOP learns semantically and visually
interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial
Intelligence (AAAI), 202
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial
Intelligence. This year, the Amazon Alexa Prize challenge was announced for the
first time, where real customers get to rate systems developed by leading
universities worldwide. The aim of the challenge is to converse "coherently and
engagingly with humans on popular topics for 20 minutes". We describe our Alexa
Prize system (called 'Alana') consisting of an ensemble of bots, combining
rule-based and machine learning systems, and using a contextual ranking
mechanism to choose a system response. The ranker was trained on real user
feedback received during the competition, where we address the problem of how
to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A
VirtualHome: Simulating Household Activities via Programs
In this paper, we are interested in modeling complex activities that occur in
a typical household. We propose to use programs, i.e., sequences of atomic
actions and interactions, as a high level representation of complex tasks.
Programs are interesting because they provide a non-ambiguous representation of
a task, and allow agents to execute them. However, nowadays, there is no
database providing this type of information. Towards this goal, we first
crowd-source programs for a variety of activities that happen in people's
homes, via a game-like interface used for teaching kids how to code. Using the
collected dataset, we show how we can learn to extract programs directly from
natural language descriptions or from videos. We then implement the most common
atomic (inter)actions in the Unity3D game engine, and use our programs to
"drive" an artificial agent to execute tasks in a simulated household
environment. Our VirtualHome simulator allows us to create a large activity
video dataset with rich ground-truth, enabling training and testing of video
understanding models. We further showcase examples of our agent performing
tasks in our VirtualHome based on language descriptions.Comment: CVPR 2018 (Oral
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
Institutional Efficiency in Independent Central Banking: A Communicative Matter?
Political economists have traditionally been indifferent to the communicative construction of money and central banking in the public sphere. It does not matter to them whether monetary affairs are rendered as a rational game over the preservation of the value of the currency or, for example, as a morality play. In this paper I will suggest that the very political economy of central bank independence requires a departure from such a practice. I will argue that the communicative articulation of the monetary game is relevant to understand how independent central banks can achieve institutional efficiency, and why they face no tradeoff between institutional efficiency and democratic legitimacy. In particular, I will suggest than an institutionally efficient central bank cannot but act as an agent of communicative empowerment of the audiences that make up its local context of operation.
The Information Content of Mandatory Disclosures
The information quality of mandatory financial reporting depends on two factors: (1) Are standards appropriate to produce financial statements that provide investors with sufficient information? (2) Is compliance to standards enforced by appropriate institutions? This paper addresses the question if firms should be able to create hidden reserves as an example for the effect of standards on information quality. The analysis shows that rational investors are able to correctly decipher financial statements – independent of the standards in use. The question of sufficient enforcement proves to have a deeper impact on the quality of information.
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