Call for Papers
IWSDS 2019 Special Session -- Dialogue systems and lifelong learning April 24-26, 2019 Siracusa, Sicily, Italy
* DSLL description
The topic of dialogue systems and chatbots has been gaining renewed interest in the recent years, particularly thanks to the recent development of deep neural networks. Nevertheless, most of the proposed approaches require a very large amounts of data, which is difficult to obtain when talking about dialogue. Proposing Methods that fill the data gap will allow data-driven dialogue systems trained in a specific domain and task to improve over time, and even learning in a cumulative way new domains or tasks is of great interest to fill the data gap. This direction of research is called lifelong learning or continuous learning. From another angle, another research paradigm that allows for continuous learning is to design systems are able to learn a new task or domain through interaction as a student with a teacher could do.
The main obective of this special session is to gather researchers interested in dialogue systems that interact with the users in order to learn about new domains or acquire new knowledge.
We invite submissions on all aspects of dialogue systems, lifelong and interactive learning.
Topics include but are not limited to:
- Dialogue systems that improve over time - Intelligent systems that use interaction to gather new information - Specific techniques that can enable learning through interaction, such as online reinforcement learning, imitation learning, etc. - Corpora for interactive learning with dialogue - Demonstration of systems that learn through interaction - Evaluation methodologies
* Session Committe
Eneko Agirre, University of the Basque Country, Spain Mark Cieliebak, Zurich University of Applied Sciences, Switzerland Olivier Galibert, LNE, France Sahar Ghannay, LIMSI, Univ. Paris Sud, France Arantxa Otegi, University of the Basque Country, Spain Anselmo Peñas, Universidad Nacional de Educación a Distancia, Spain Camille Pradel, Synapse Développement Sophie Rosset, LIMSI, CNRS, France Anne Vilnat, LIMSI, Univ. Paris Sud, France
* Important dates
Submission paper: January 15 Author notification: January 25 Camera ready: February 15
For paper submission process, please check the IWSDS 2019 website <https://iwsds2019.unikore.it/> and the submission paper website <https://easychair.org/conferences/?conf=iwsds2019>
Artificial intelligence has made significant advances on solving prediction and dialogue tasks. But most of the approaches are based on off-line and supervised learning, where algorithms take as input annotated data and build a model. Further work is necessary to build autonomous agents which are capable of learning from the environment and the interactions, without explicit supervision for each new task. The goal of Lifelong Learning (LL, also known as Learning to Learn) is to research methods for continuous learning of various tasks over time and learning commonalities among them (Chen and Liu, 2018). Current LL systems exploit similarities between the learned models for past tasks using task meta-features (Eaton and Ruvolo, 2013) and corresponding methods to learn representations of tasks, using for instance neural networks and ensembles of learners. Still, LL assumes that manual annotations exist for each item to be learned, while autonomous agents rarely have access to such supervision. In a realistic scenario the agent receives feedback only after completing a complex task comprising of several decisions, and needs to guess which of the decisions were correct or incorrect.
Current interactions between humans and computers are limited to constrained dialogues, where dialogue systems (aka ChatBots or Conversational Agents) are trained on a number of annotated sample dialogues of a narrow domain. The development cost is considerable, both in building the representation of the knowledge for the target domain and in the dialogue management proper, where one of the most important shortcomings is the variability of human language and the large amount of background knowledge that needs to be shared for effective dialogue. In addition, most of the learned knowledge needs to be learned nearly from scratch for each new dialogue task, including both the domain knowledge (learned using knowledge induction or knowledge bases) and the dialogue management module (adapted to the new domain). Interestingly, humans use dialogue to improve their own knowledge of a domain. That is, people interact with other people in order to confirm, retract or refine their understanding. This topic of learning through dialogue is an emerging one with more and more attempts to propose framework and tasks to evaluate such system. Most recent work in this area concern learning through conversation where the supervision part is given by the user feedback (Weston, 2016), the way the learning system can ask questions in an online reinforcement learning framework (Li et al., 2017) and also on how to learn and infer new knowledge during a dialogue (Mazumder et al., 2018 ; Letard et al., 2016).
This special session will focus on methods and evaluation methodologies for learning through dialogue. All aspects involved in dialogue (natural language understanding, dialogue management, natural language generation, knowledge management) are of interest.
This special session will provide a focal point for the growing research community on interactive learning with and by dialogue.
Z. Chen, and B. Liu. Lifelong Machine Learning (2nd Edition). Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers. August 2018, 207p
E. Eaton and P. L. Ruvolo. 2013. ELLA: An efficient lifelong learning algorithm. In ICML 2013.
Sahisnu Mazumder, Nianzu Ma, Bing Liu. Towards a Continuous Knowledge Learning Engine for Chatbots. arXiv:1802.06024 [cs.CL], 16 Feb. 2018.
Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston, Learning through Dialogue Interactions by Asking Questions, ICLR 2017.
Jason E. Weston. Dialog-based language learning. NIPS 2016.
Vincent Letard, Sophie Rosset, Gabriel Illouz. Incremental Learning From Scratch Using Analogical Reasoning. ICTAI 2016.