146 research outputs found
A Type-coherent, Expressive Representation as an Initial Step to Language Understanding
A growing interest in tasks involving language understanding by the NLP
community has led to the need for effective semantic parsing and inference.
Modern NLP systems use semantic representations that do not quite fulfill the
nuanced needs for language understanding: adequately modeling language
semantics, enabling general inferences, and being accurately recoverable. This
document describes underspecified logical forms (ULF) for Episodic Logic (EL),
which is an initial form for a semantic representation that balances these
needs. ULFs fully resolve the semantic type structure while leaving issues such
as quantifier scope, word sense, and anaphora unresolved; they provide a
starting point for further resolution into EL, and enable certain structural
inferences without further resolution. This document also presents preliminary
results of creating a hand-annotated corpus of ULFs for the purpose of training
a precise ULF parser, showing a three-person pairwise interannotator agreement
of 0.88 on confident annotations. We hypothesize that a divide-and-conquer
approach to semantic parsing starting with derivation of ULFs will lead to
semantic analyses that do justice to subtle aspects of linguistic meaning, and
will enable construction of more accurate semantic parsers.Comment: Accepted for publication at The 13th International Conference on
Computational Semantics (IWCS 2019
Get the gist? Using large language models for few-shot decontextualization
In many NLP applications that involve interpreting sentences within a rich
context -- for instance, information retrieval systems or dialogue systems --
it is desirable to be able to preserve the sentence in a form that can be
readily understood without context, for later reuse -- a process known as
``decontextualization''. While previous work demonstrated that generative
Seq2Seq models could effectively perform decontextualization after being
fine-tuned on a specific dataset, this approach requires expensive human
annotations and may not transfer to other domains. We propose a few-shot method
of decontextualization using a large language model, and present preliminary
results showing that this method achieves viable performance on multiple
domains using only a small set of examples
We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses
Many practical applications of dialogue technology require the generation of
responses according to a particular developer-specified persona. While a
variety of personas can be elicited from recent large language models, the
opaqueness and unpredictability of these models make it desirable to be able to
specify personas in an explicit form. In previous work, personas have typically
been represented as sets of one-off pieces of self-knowledge that are retrieved
by the dialogue system for use in generation. However, in realistic human
conversations, personas are often revealed through story-like narratives that
involve rich habitual knowledge -- knowledge about kinds of events that an
agent often participates in (e.g., work activities, hobbies, sporting
activities, favorite entertainments, etc.), including typical goals,
sub-events, preconditions, and postconditions of those events. We capture such
habitual knowledge using an explicit schema representation, and propose an
approach to dialogue generation that retrieves relevant schemas to condition a
large language model to generate persona-based responses. Furthermore, we
demonstrate a method for bootstrapping the creation of such schemas by first
generating generic passages from a set of simple facts, and then inducing
schemas from the generated passages
A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient
A schema-guided approach to dialogue management has been shown in recent work
to be effective in creating robust customizable virtual agents capable of
acting as friendly peers or task assistants. However, successful applications
of these methods in open-ended, mixed-initiative domains remain elusive --
particularly within medical domains such as virtual standardized patients,
where such complex interactions are commonplace -- and require more extensive
and flexible dialogue management capabilities than previous systems provide. In
this paper, we describe a general-purpose schema-guided dialogue management
framework used to develop SOPHIE, a virtual standardized cancer patient that
allows a doctor to conveniently practice for interactions with patients. We
conduct a crowdsourced evaluation of conversations between medical students and
SOPHIE. Our agent is judged to produce responses that are natural, emotionally
appropriate, and consistent with her role as a cancer patient. Furthermore, it
significantly outperforms an end-to-end neural model fine-tuned on a human
standardized patient corpus, attesting to the advantages of a schema-guided
approach
Validating a virtual human and automated feedback system for training doctor-patient communication skills
Effective communication between a clinician and their patient is critical for
delivering healthcare maximizing outcomes. Unfortunately, traditional
communication training approaches that use human standardized patients and
expert coaches are difficult to scale. Here, we present the development and
validation of a scalable, easily accessible, digital tool known as the
Standardized Online Patient for Health Interaction Education (SOPHIE) for
practicing and receiving feedback on doctor-patient communication skills.
SOPHIE was validated by conducting an experiment with 30 participants. We found
that participants who underwent SOPHIE performed significantly better than the
control in overall communication, aggregate scores, empowering the patient, and
showing empathy ( in all cases). One day, we hope that SOPHIE will
help make communication training resources more accessible by providing a
scalable option to supplement existing resources.Comment: 10 pages, 5 figures, 2 table
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