35,210 research outputs found
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
A number of recent works have proposed techniques for end-to-end learning of
communication protocols among cooperative multi-agent populations, and have
simultaneously found the emergence of grounded human-interpretable language in
the protocols developed by the agents, all learned without any human
supervision!
In this paper, using a Task and Tell reference game between two agents as a
testbed, we present a sequence of 'negative' results culminating in a
'positive' one -- showing that while most agent-invented languages are
effective (i.e. achieve near-perfect task rewards), they are decidedly not
interpretable or compositional.
In essence, we find that natural language does not emerge 'naturally',
despite the semblance of ease of natural-language-emergence that one may gather
from recent literature. We discuss how it is possible to coax the invented
languages to become more and more human-like and compositional by increasing
restrictions on how two agents may communicate.Comment: 9 pages, 7 figures, 2 tables, accepted at EMNLP 2017 as short pape
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
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Crowd-powered conversational assistants have been shown to be more robust
than automated systems, but do so at the cost of higher response latency and
monetary costs. A promising direction is to combine the two approaches for high
quality, low latency, and low cost solutions. In this paper, we introduce
Evorus, a crowd-powered conversational assistant built to automate itself over
time by (i) allowing new chatbots to be easily integrated to automate more
scenarios, (ii) reusing prior crowd answers, and (iii) learning to
automatically approve response candidates. Our 5-month-long deployment with 80
participants and 281 conversations shows that Evorus can automate itself
without compromising conversation quality. Crowd-AI architectures have long
been proposed as a way to reduce cost and latency for crowd-powered systems;
Evorus demonstrates how automation can be introduced successfully in a deployed
system. Its architecture allows future researchers to make further innovation
on the underlying automated components in the context of a deployed open domain
dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human
Factors in Computing Systems 2018 (CHI'18
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