AI*QA 2018 : 1st International Workshop on Artificial Intelligence for Question Answering
Call For Papers
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CALL FOR PAPERS
1st International Workshop on Artificial Intelligence for Question Answering
September 2, 2018
In conjunction with the 22nd European Conference on
Advances in Databases and Information Systems (ADBIS 2018)
Keywords: Artificial Intelligence, Question Answering, Deep Learning, Natural Language Processing, Natural Language Understanding, Entity Extraction, Sentiment Analysis, Machine Translation, Agents, Dialogue, Conversational Agents, Conversational Interfaces, Chatbot, Social bot, Human-machine interface, Conversational interface, Knowledge Base, Knowledge Graph, Logics, Reasoning, Query Language, Ontology, Neural Network, Deep Neural Network, Learning, Reinforcement Learning, Deep Insight Engine, Information Retrieval, Information Extraction
Paper Submission: (Extended) May 16, 2018
Acceptance Notification: June 06, 2018
Camera-ready Submission: June 18, 2018
Workshop: September 02, 2018
Submission link: https://easychair.org/conferences/?conf=aiqa2018
Authors are invited to submit electronically original contributions in English, carefully checked for correct grammar and spelling, addressing one or several topics of interest. Each paper should clearly indicate the nature of its main contribution. Work in progress or discussion about ideas, methods, and initial experimental results are also welcome.
Two types of papers are solicited:
* Regular papers should be submitted for review with around 8 pages (12 pages max).
* Short paper / demos / work in progress should be submitted for review with around 4 pages (6 pages max).
Submitted papers must comply with the LNCS formatting guidelines available at http://www.springer.com/series/7899 (see the link "Instructions for Authors" on the right-hand side). Papers must be submitted electronically in PDF, using EasyChair system: https://easychair.org/conferences/?conf=aiqa2018
AI*QA 2018 proceedings will be published in Springer’s Communications in Computer and Information Science (Springer) series.
We plan to invite selected workshop papers for publication in an international journal.
At least one author of each accepted paper must register and attend the workshop to present the paper.
Artificial Intelligence (AI) is attracting much attention and it will be a major driver of technology in the coming years. It will bring a big transformation to many industries, such as transportation, manufacturing, healthcare, communications, financial services, and more. This is possible because of the big data availability, the advances in hardware capabilities and the inventions of new models, methods, algorithms capable to offer new solutions for long-standing research problems.
Question Answering (QA) is a complex task that requires the ability to understand the natural language (NLU) and to reason over relevant contexts. Almost all Natural Language Processing (NLP) tasks can be seen as QA problem (e.g. entity extraction, sentiment analysis, machine translation).
Recently, QA by using novel AI techniques has seen scientific and commercial popularity that attracted media attention, but effective QA is a challenging task for machines that try to simulate the human behaviour.
Some solutions are based on Information Retrieval (IR) techniques, other on Information Extraction (IE) processes that enable to create Knowledge Bases (KBs), so logic-based query languages are used to infer answers from KBs. KB-based solutions can be satisfactory for closed-domain problems, but they are unlikely to scale up to answer questions on any topic.
Novel approaches for QA over documents are based on Deep Neural Networks that encode the documents and the questions to determine the answers.
A lot of research has focused on learning from fixed training sets of labeled data, but other try to learn through online interaction (dialogue) with humans or other agents. This is the case of conversational agents (or conversational interfaces/bots/chatbots) that adapt their model based on teacher's feedbacks (Reinforcement Learning) and change beliefs in response to new information.
The purpose of the AI*QA 2018 workshop is to bring together researchers, engineers, and practitioners interested in the theory and applications related to the Question Answering (QA) problem by using Artificial Intelligence (AI) techniques. The aim is to better understand the advantages and the limitations of proposed solutions and systems in different domains and situations by stimulating and facilitating through the workshop an active exchange, interaction, and comparison of approaches, methods, tools, and ideas.
LIST OF TOPICS
Topics of interest include but are not limited to:
* Theoretical models for answering questions
-- Rule-based models
-- Logic-based models
-- IR-based models
-- Probabilistic models
-- Graph-based models
-- Deep Learning models
-- Reinforcement‐based Learning models
-- Belief‐based Learning Models
-- Hybrid models
* Algorithms and methods
-- Improving the learning process through dialogue interactions for natural language comprehension: human in the loop, reinforcement learning, conversational agents or bots
-- Reasoning on KBs to help infer answers to complex questions: knowledge representation and reasoning, logical agents
-- Information Extraction, Information Retrieval, and Semantic Search and Labeling to enable answering of questions
-- Hybrid Methods for implementing solutions that enable to answer questions
* Databases and knowledge representations
-- Knowledge Bases (KBs): answering questions by exploiting KBs
-- Document / raw text: natural language comprehension and question answering
-- Hybrid Databases as sources exploited for answering questions
-- Ontologies that provides a common vocabulary used to state facts and formulate questions about the domain
* Tools and solutions
-- Deep Insight Engines that exploit question answering techniques
-- Frameworks to validate results in question answering
-- Social bots able to automatically produce responses through natural language algorithms
-- Question answering systems that integrate different modules, also exploiting existing tools
-- User interfaces for simplifying human-machine interaction and answering questions
-- Conversational interfaces implemented, for instance, by Natural Language Understanding services
* Evaluation of results
-- Evaluation measures of the quality of the answers
-- Experiment design: planning a study to meet specified objectives in Question Answering solutions
-- Comparison of approaches related to the question answering problem
-- Empirical evaluation algorithms or systems for Question Answering
-- Evaluation of Natural Language Understanding services for (conversational) Question Answering Systems
-- Corpus and Ground-Truth construction, and their publication for problems related to the evaluation of question answering approaches
* Application to domains
-- AI techniques applied to solve Question Answering problems or to implement chatbots applied in different areas of, but not limited to: finance, marketing, e-commerce, health care, transportation, internet of things, tourism. Can be presented needs, proposed solutions, case studies, benefits and related experiences
Giuseppe Riccardi (University of Trento, Italy)
Muhammad Arif (University of Malaya, Malaysia)
Alexandra Balahur (European Commission Joint Research Centre, Italy)
Ioannis Hatzilygeroudis (University of Patras, Greece)
Ivan Jureta (University of Namur, Belgium)
Joohyung Lee (Arizona State University, Arizona, USA)
Marco Leo (Institute of Optics, CNR, Italy)
Yuan-Fang Li (Monash University, Australia)
Olga Kalimullina (National Research University ITMO, Russia)
Thomas Meyer (University of Cape Town and CAIR, South Africa)
Guenter Neumann (German Research Center for Artificial Intelligence, DFKI, Germany)
Rafael Peñaloza (Free University of Bozen-Bolzano, Italy)
David Schlangen (Bielefeld University, Germany)
Koichi Takeda (Nagoya University, Japan)
Wei Zhang (IBM Research AI, NY, USA)
Ermelinda Oro (National Research Council, Italy)
Massimo Ruffolo (National Research Council, Italy)
Eduardo Fermè (University of Madeira, Portugal)
The conference will be held in Danubius Hotel Flamenco ****, Wellness and conference hotel Budapest, Hungary
Address: Budapest, Tas vezér u. 3-7, 1113
SPONSORS & SUPPORTERS
* ICAR-CNR: Institute for high performance computing and networking of the National Research Council of Italy, https://www.icar.cnr.it/en/
* Altilia srl: a Machine Intelligence company, http://www.altiliagroup.com/
For any further question or information request on AI*AQ 2018 workshop, please send an email to:
Workshop website: http://aiqa2018.icar.cnr.it/
Follow us on Twitter: https://twitter.com/aiqa2018
Local Arrangements: Altagra Business Services and Travel Agency Ltd. e-mail: firstname.lastname@example.org, Phone:+36 28 419 647, Fax: +36 28 432 985
Submit your paper! Join us to share, discuss, learn, network and collaborate about AI*QA and visit Budapest!
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