10,882 research outputs found
Task-oriented conversational agent self-learning based on sentiment analysis
One of the biggest issues in creating a task-oriented conversational agent with natural language processing based on machine learning comes from size and correctness of the training dataset. It could take months or even years of data collection and the resulting static resource may get soon out of date thus requiring a significant amount of work to supervise it. To overcome these difficulties, we implemented an algorithm with the ability of improving learning efficiency based on the emotions and reactions arising from the conversation between a user and the bot, automatically and in real time. To this end, we have studied an error function that, as in any closed loop control system, corrects the input to improve the output. The proposed method is based on both calibrating the interpretation given to the initial dataset and expanding the dictionary with new terms. Thanks to this innovative approach, the satisfaction of the interlocutors is higher if compared to algorithms with a static dataset or with semi-automatic self-learning rules
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
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