19,461 research outputs found
Neural Response Ranking for Social Conversation: A Data-Efficient Approach
The overall objective of 'social' dialogue systems is to support engaging,
entertaining, and lengthy conversations on a wide variety of topics, including
social chit-chat. Apart from raw dialogue data, user-provided ratings are the
most common signal used to train such systems to produce engaging responses. In
this paper we show that social dialogue systems can be trained effectively from
raw unannotated data. Using a dataset of real conversations collected in the
2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good'
system responses to user utterances, i.e. responses which are likely to lead to
long and engaging conversations. We show that (1) our neural ranker
consistently outperforms several strong baselines when trained to optimise for
user ratings; (2) when trained on larger amounts of data and only using
conversation length as the objective, the ranker performs better than the one
trained using ratings -- ultimately reaching a Precision@1 of 0.87. This
advance will make data collection for social conversational agents simpler and
less expensive in the future.Comment: 2018 EMNLP Workshop SCAI: The 2nd International Workshop on
Search-Oriented Conversational AI. Brussels, Belgium, October 31, 201
The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents
We introduce the StatCan Dialogue Dataset consisting of 19,379 conversation
turns between agents working at Statistics Canada and online users looking for
published data tables. The conversations stem from genuine intents, are held in
English or French, and lead to agents retrieving one of over 5000 complex data
tables. Based on this dataset, we propose two tasks: (1) automatic retrieval of
relevant tables based on a on-going conversation, and (2) automatic generation
of appropriate agent responses at each turn. We investigate the difficulty of
each task by establishing strong baselines. Our experiments on a temporal data
split reveal that all models struggle to generalize to future conversations, as
we observe a significant drop in performance across both tasks when we move
from the validation to the test set. In addition, we find that response
generation models struggle to decide when to return a table. Considering that
the tasks pose significant challenges to existing models, we encourage the
community to develop models for our task, which can be directly used to help
knowledge workers find relevant tables for live chat users.Comment: Accepted at EACL 202
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Towards Emotion-Sensitive Conversational User Interfaces in Healthcare Applications
Perception of emotions and adequate responses are key factors of a successful conversational agent. However, determining emotions in a healthcare setting depends on multiple factors such as context and medical condition. Given the increase of interest in conversational agents integrated in mobile health applications, our objective in this work is to introduce a concept for analyzing emotions and sentiments expressed by a person in a mobile health application with a conversational user interface. The approach bases upon bot technology (Synthetic intelligence markup language) and deep learning for emotion analysis. More specifically, expressions referring to sentiments or emotions are classified along seven categories and three stages of strengths using treebank annotation and recursive neural networks. The classification result is used by the chatbot for selecting an appropriate response. In this way, the concerns of a user can be better addressed. We describe three use cases where the approach could be integrated to make the chatbot emotion-sensitive
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