13,161 research outputs found
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
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
Interpretation of Natural Language Rules in Conversational Machine Reading
Most work in machine reading focuses on question answering problems where the
answer is directly expressed in the text to read. However, many real-world
question answering problems require the reading of text not because it contains
the literal answer, but because it contains a recipe to derive an answer
together with the reader's background knowledge. One example is the task of
interpreting regulations to answer "Can I...?" or "Do I have to...?" questions
such as "I am working in Canada. Do I have to carry on paying UK National
Insurance?" after reading a UK government website about this topic. This task
requires both the interpretation of rules and the application of background
knowledge. It is further complicated due to the fact that, in practice, most
questions are underspecified, and a human assistant will regularly have to ask
clarification questions such as "How long have you been working abroad?" when
the answer cannot be directly derived from the question and text. In this
paper, we formalise this task and develop a crowd-sourcing strategy to collect
32k task instances based on real-world rules and crowd-generated questions and
scenarios. We analyse the challenges of this task and assess its difficulty by
evaluating the performance of rule-based and machine-learning baselines. We
observe promising results when no background knowledge is necessary, and
substantial room for improvement whenever background knowledge is needed.Comment: EMNLP 201
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