510,047 research outputs found
The Language of Dialogue Is Complex
Integrative Complexity (IC) is a psychometric that measures the ability of a
person to recognize multiple perspectives and connect them, thus identifying
paths for conflict resolution. IC has been linked to a wide variety of
political, social and personal outcomes but evaluating it is a time-consuming
process requiring skilled professionals to manually score texts, a fact which
accounts for the limited exploration of IC at scale on social media.We combine
natural language processing and machine learning to train an IC classification
model that achieves state-of-the-art performance on unseen data and more
closely adheres to the established structure of the IC coding process than
previous automated approaches. When applied to the content of 400k+ comments
from online fora about depression and knowledge exchange, our model was capable
of replicating key findings of prior work, thus providing the first example of
using IC tools for large-scale social media analytics.Comment: 12 pages, 9 figures, 10 table
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Dialogue Games for Crosslingual Communication
We describe a novel approach to crosslingual dialogue that supports highly accurate communication of semantically complex content between people who do not speak the same language. The approach is introduced through an implemented application that covers the same ground as the chapter of a conventional phrase book for food shopping. We position the approach with respect to dialogue systems and Machine Translation-based approaches to crosslingual dialogue. The current work is offered as a first step towards the innovative use of dialogue theories for the enhancement of human–human dialogue
Conjunctive Visual and Auditory Development via Real-Time Dialogue
Human developmental learning is capable of
dealing with the dynamic visual world, speech-based
dialogue, and their complex real-time association.
However, the architecture that realizes
this for robotic cognitive development has
not been reported in the past. This paper takes
up this challenge. The proposed architecture does
not require a strict coupling between visual and
auditory stimuli. Two major operations contribute
to the “abstraction” process: multiscale temporal
priming and high-dimensional numeric abstraction
through internal responses with reduced variance.
As a basic principle of developmental learning,
the programmer does not know the nature
of the world events at the time of programming
and, thus, hand-designed task-specific representation
is not possible. We successfully tested the
architecture on the SAIL robot under an unprecedented
challenging multimodal interaction mode:
use real-time speech dialogue as a teaching source
for simultaneous and incremental visual learning
and language acquisition, while the robot is viewing
a dynamic world that contains a rotating object
to which the dialogue is referring
Learning About Meetings
Most people participate in meetings almost every day, multiple times a day.
The study of meetings is important, but also challenging, as it requires an
understanding of social signals and complex interpersonal dynamics. Our aim
this work is to use a data-driven approach to the science of meetings. We
provide tentative evidence that: i) it is possible to automatically detect when
during the meeting a key decision is taking place, from analyzing only the
local dialogue acts, ii) there are common patterns in the way social dialogue
acts are interspersed throughout a meeting, iii) at the time key decisions are
made, the amount of time left in the meeting can be predicted from the amount
of time that has passed, iv) it is often possible to predict whether a proposal
during a meeting will be accepted or rejected based entirely on the language
(the set of persuasive words) used by the speaker
Individual and Domain Adaptation in Sentence Planning for Dialogue
One of the biggest challenges in the development and deployment of spoken
dialogue systems is the design of the spoken language generation module. This
challenge arises from the need for the generator to adapt to many features of
the dialogue domain, user population, and dialogue context. A promising
approach is trainable generation, which uses general-purpose linguistic
knowledge that is automatically adapted to the features of interest, such as
the application domain, individual user, or user group. In this paper we
present and evaluate a trainable sentence planner for providing restaurant
information in the MATCH dialogue system. We show that trainable sentence
planning can produce complex information presentations whose quality is
comparable to the output of a template-based generator tuned to this domain. We
also show that our method easily supports adapting the sentence planner to
individuals, and that the individualized sentence planners generally perform
better than models trained and tested on a population of individuals. Previous
work has documented and utilized individual preferences for content selection,
but to our knowledge, these results provide the first demonstration of
individual preferences for sentence planning operations, affecting the content
order, discourse structure and sentence structure of system responses. Finally,
we evaluate the contribution of different feature sets, and show that, in our
application, n-gram features often do as well as features based on higher-level
linguistic representations
Fostering pre-service EFL teachers’ communicative competence through role-playing games
The quality of foreign language teaching (FLT) is inextricably linked with developing students’ communicative competence (CC), a complex formation that is being comprehended by methodologists. The term “communicative competence” (CC) implies the speaker's production of grammatically correct language appropriate to various social settings while observing the linguistic and social rules of native speakers. Despite the complex structure of developing English as a foreign language (EFL) learners' CC, one of the conditions for its successful course is a systemic and technological approach to role-playing games (RPG) in teaching dialogue speech. The study aims to examine the development of EFL students' CC and the effects of RPG in teaching dialogue speech. To test the hypothesis that a systemic and technological approach to using RPG in teaching dialogue speech fosters the pre-service EFL teachers’ CC, the research methodology employed a semi-experimental pretest-posttest control group design with pre-service EFL teachers. Participants (n=36) were divided into experimental (n=18) and control (n=18) groups. The results of the posttest experiment confirmed the effectiveness of systemic and technological approaches to the use of RPG in significantly increasing the pre-service EFL teachers’ CC
Transfer Learning for Context-Aware Spoken Language Understanding
Spoken language understanding (SLU) is a key component of task-oriented
dialogue systems. SLU parses natural language user utterances into semantic
frames. Previous work has shown that incorporating context information
significantly improves SLU performance for multi-turn dialogues. However,
collecting a large-scale human-labeled multi-turn dialogue corpus for the
target domains is complex and costly. To reduce dependency on the collection
and annotation effort, we propose a Context Encoding Language Transformer
(CELT) model facilitating exploiting various context information for SLU. We
explore different transfer learning approaches to reduce dependency on data
collection and annotation. In addition to unsupervised pre-training using
large-scale general purpose unlabeled corpora, such as Wikipedia, we explore
unsupervised and supervised adaptive training approaches for transfer learning
to benefit from other in-domain and out-of-domain dialogue corpora.
Experimental results demonstrate that the proposed model with the proposed
transfer learning approaches achieves significant improvement on the SLU
performance over state-of-the-art models on two large-scale single-turn
dialogue benchmarks and one large-scale multi-turn dialogue benchmark.Comment: 6 pages, 3 figures, ASRU201
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