23,125 research outputs found
Contextual Language Model Adaptation for Conversational Agents
Statistical language models (LM) play a key role in Automatic Speech
Recognition (ASR) systems used by conversational agents. These ASR systems
should provide a high accuracy under a variety of speaking styles, domains,
vocabulary and argots. In this paper, we present a DNN-based method to adapt
the LM to each user-agent interaction based on generalized contextual
information, by predicting an optimal, context-dependent set of LM
interpolation weights. We show that this framework for contextual adaptation
provides accuracy improvements under different possible mixture LM partitions
that are relevant for both (1) Goal-oriented conversational agents where it's
natural to partition the data by the requested application and for (2) Non-goal
oriented conversational agents where the data can be partitioned using topic
labels that come from predictions of a topic classifier. We obtain a relative
WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass
decoding framework, over an unadapted model. We also show up to a 15% relative
improvement in recognizing named entities which is of significant value for
conversational ASR systems.Comment: Interspeech 2018 (accepted
Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies
Advances in artificial intelligence have renewed interest in conversational
agents. So-called chatbots have reached maturity for industrial applications.
German insurance companies are interested in improving their customer service
and digitizing their business processes. In this work we investigate the
potential use of conversational agents in insurance companies by determining
which classes of agents are of interest to insurance companies, finding
relevant use cases and requirements, and developing a prototype for an
exemplary insurance scenario. Based on this approach, we derive key findings
for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International
Conference on Agents and Artificial Intelligence 2019 (ICAART 2019
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
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