IWDLQ2021: First International Workshop on Deep Learning for Question Answering
Question Answering System (QAS) is an important area in Artificial Intelligence.
Generating automatic response is a fastidious and time-consuming task for there exists
only some very general approaches to understand the intent of users. Question Answering (QA) is applied in many domain applications such as medical, finance, e-commerce,
etc. Given a list of documents, a QAS can provide the right answer to the query pose in
natural language. It combines natural language processing (NLP), information retrieval
(IR) and knowledge representation and reasoning (KRR) as a relevant component for
this process. The general process of QA is composed of different steps: (i) user query,
(ii) question analysis (simple or complex query, open or closed domain, linguistic layer,
semantic layer, etc), (iii) answer retrieval, and (iv) answer extraction from a set of candidate ones. All these steps are important to answer correctly, precisely and briefly to the
user native language question. The answer can refer to a term, a sentence, an image, an
audio, a video or to the full textual document.
Recent Deep Learning approaches and information retrieval are implemented in order to
reason over the questions and its links with the corresponding response. Modern NLP
techniques makes it possible for computers to read and interpret text, hear and understand
speech, measure sentiment, and determine which parts in a document are important.
Another important aspect of QAS is the integration of knowledge graphs (KGs) as a
new dimension to provide a concise answer issued from the KG. KG is graph-based data
model that structure and store real-world entities (abstract concepts) and their relationships (hierarchical and associative) in a graph. The KG is the most suitable and beneficial
way to solve many challenging problems related to information domain.
The first edition of this workshop aims at highlighting recent and future advances on
question answering systems over structured semantic and unstructured textual data and
to demonstrate the role of deep learning algorithms to enrich this process. In addition
to that, the goal of this workshop is to bring together an area for experts from industry, science and academia to exchange ideas and discuss results of on-going research in
Question Answering approaches.
Topics of interests: We invite the submission of original works that is related, but are
not limited, to the topics below:
• Question answering over Linked Data
• Knowledge Graphs for Question Answering
• Complex Question Answering over texts and linked data
• Reasoning for Complex Question Answering
• Natural Language Processing based question answering
• Hybrid text and knowledge graph reasoning
• SPARQL query pattern generation
• Natural language querying of RDF exposed as Linked Data
• Ontology-based query answering
• Visual Question Answering
• Image question answering
• Audio and Speech Question Answering
• Video Question Answering
• Datasets combining structured and unstructured knowledge
• Applications of question answering
• and so on.
Submission: The workshop is open to submit unpublished work resulted from research that present original scientific result, methodological aspects, concepts and approaches. All submissions must be PDF documents written in English and formatted according to KGSWC21 format . Papers are to be submitted through the Easychair Conference Management System. Please note that paper submissions are anonymous.
We welcome the following types of contributions:
• Full research papers (9-12 pages): Finished or consolidated R&D works, to be
included in one of the Workshop themes
• Short papers (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion.
At least one author of each accepted paper must register for the workshop, in order to present the paper, there, and to the conference. For further instructions please refer to the KGSWC 2021 page (https://kgswc.org/).
• Workshop paper submission due: October 01, 2021
• Workshop paper notifications: October 23, 2021
• Workshop paper camera-ready versions due: November 02, 2021
• Workshop: November 19-20, 2021 (half-day)
• Estimation of the number of attendees: 50
All deadlines are 23:59 anywhere on earth (UTC-12).
Sarra BEN ABBES, Research Scientist, ENGIE-France: email@example.com
Rim Hantach, Research Scientist, ENGIE-France: firstname.lastname@example.org
Philippe Calvez, Research Lab Manager, ENGIE-France: email@example.com
Keynote Speakers: –TBD
Program Committee: – TBD
International Workshop on Deep Learning for Question Answering