163 research outputs found
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
Sequence generation models for dialogue are known to have several problems:
they tend to produce short, generic sentences that are uninformative and
unengaging. Retrieval models on the other hand can surface interesting
responses, but are restricted to the given retrieval set leading to erroneous
replies that cannot be tuned to the specific context. In this work we develop a
model that combines the two approaches to avoid both their deficiencies: first
retrieve a response and then refine it -- the final sequence generator treating
the retrieval as additional context. We show on the recent CONVAI2 challenge
task our approach produces responses superior to both standard retrieval and
generation models in human evaluations
An Assessment of Mental Health Policies and Services At the University of Southern Maine (Portland and Gorham campuses)
In the spring of 2013, a USM Muskie Graduate student conducted an assessment of mental health policies and services at the University of Southern Maine to help inform the University how it might better meet the mental health needs of its students. These pressures can place a university’s obligations to educate students and to meet their health needs in conflict with each other. The assessment involved in-depth interviews with 11 individuals in departments who were identified as having an important role in addressing student mental health needs
A Review of Concussion Recognition, Assessment and Management for Paramedics
Although paramedics are trained in the recognition and management of traumatic brain injuries, the management of the patient with a concussion is a clinical presentation that may not be addressed in sufficient detail within paramedic education, and paramedics may be unaware of the latest evidence-based concussion treatment guidelines and recommendations. While life-support measures are rarely required in concussion injuries, it would be prudent for paramedics to familiarize themselves with this evolving area of concern in medicine. This brief overview aims to address these potential issues
Learning to Speak and Act in a Fantasy Text Adventure Game
We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully
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