1,801 research outputs found
Crowdsourcing for Reminiscence Chatbot Design
In this work-in-progress paper we discuss the challenges in identifying
effective and scalable crowd-based strategies for designing content,
conversation logic, and meaningful metrics for a reminiscence chatbot targeted
at older adults. We formalize the problem and outline the main research
questions that drive the research agenda in chatbot design for reminiscence and
for relational agents for older adults in general
Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
Using supporting backchannel (BC) cues can make human-computer interaction
more social. BCs provide a feedback from the listener to the speaker indicating
to the speaker that he is still listened to. BCs can be expressed in different
ways, depending on the modality of the interaction, for example as gestures or
acoustic cues. In this work, we only considered acoustic cues. We are proposing
an approach towards detecting BC opportunities based on acoustic input features
like power and pitch. While other works in the field rely on the use of a
hand-written rule set or specialized features, we made use of artificial neural
networks. They are capable of deriving higher order features from input
features themselves. In our setup, we first used a fully connected feed-forward
network to establish an updated baseline in comparison to our previously
proposed setup. We also extended this setup by the use of Long Short-Term
Memory (LSTM) networks which have shown to outperform feed-forward based setups
on various tasks. Our best system achieved an F1-Score of 0.37 using power and
pitch features. Adding linguistic information using word2vec, the score
increased to 0.39
Virtual Assistant That Provides Answers Based On Past Conversations
It is often the case that participants in a conversation, e.g., a chat conversation via a messaging app or an audio/video call, do not remember the details of the conversation. This disclosure describes the use of machine learning techniques to automatically answer questions related to a past conversation by use of machine reading comprehensive models. The models are trained using conversation transcripts and provide answers based on a conversation between users, or between a user and a virtual assistant. The models can be incorporated in a virtual assistant that can reason over any number of conversations to arrive at an answer
Virtual Assistant That Provides Answers Based On Past Conversations
It is often the case that participants in a conversation, e.g., a chat conversation via a messaging app or an audio/video call, do not remember the details of the conversation. This disclosure describes the use of machine learning techniques to automatically answer questions related to a past conversation by use of machine reading comprehensive models. The models are trained using conversation transcripts and provide answers based on a conversation between users, or between a user and a virtual assistant. The models can be incorporated in a virtual assistant that can reason over any number of conversations to arrive at an answer
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
Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
Neural conversational models require substantial amounts of dialogue data for
their parameter estimation and are therefore usually learned on large corpora
such as chat forums or movie subtitles. These corpora are, however, often
challenging to work with, notably due to their frequent lack of turn
segmentation and the presence of multiple references external to the dialogue
itself. This paper shows that these challenges can be mitigated by adding a
weighting model into the architecture. The weighting model, which is itself
estimated from dialogue data, associates each training example to a numerical
weight that reflects its intrinsic quality for dialogue modelling. At training
time, these sample weights are included into the empirical loss to be
minimised. Evaluation results on retrieval-based models trained on movie and TV
subtitles demonstrate that the inclusion of such a weighting model improves the
model performance on unsupervised metrics.Comment: Accepted to SIGDIAL 201
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while
incorporating knowledge about the user into the conversation. In this work we
address the acquisition of such knowledge, for personalization in downstream
Web applications, by extracting personal attributes from conversations. This
problem is more challenging than the established task of information extraction
from scientific publications or Wikipedia articles, because dialogues often
give merely implicit cues about the speaker. We propose methods for inferring
personal attributes, such as profession, age or family status, from
conversations using deep learning. Specifically, we propose several Hidden
Attribute Models, which are neural networks leveraging attention mechanisms and
embeddings. Our methods are trained on a per-predicate basis to output rankings
of object values for a given subject-predicate combination (e.g., ranking the
doctor and nurse professions high when speakers talk about patients, emergency
rooms, etc). Experiments with various conversational texts including Reddit
discussions, movie scripts and a collection of crowdsourced personal dialogues
demonstrate the viability of our methods and their superior performance
compared to state-of-the-art baselines.Comment: published in WWW'1
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