[Event at CIG] [CFP][CIKM 2021] 1st International workshop on REthinking PAssage Retrieval for Question-Answering (REPARQA)

[rafika boutalbi]

Dear colleagues and researchers,



*1st International workshop on *REthinking PAssage Retrieval for
Question-Answering (REPARQA)



*https://sites.google.com/view/reparqaworkshopcikm2021/home
<https://sites.google.com/view/reparqaworkshopcikm2021/home>*

*Deadline: July 15, 2021*


As part of The 30th ACM International Conference on Information and
Knowledge Management (CIKM 2021)



https://www.cikm2021.org/



November 1-5, 2021, Online
Context

Research on Question-Answering (QA) systems has recently achieved
considerable success in simplified closed-domain settings such as the SQuAD
dataset, which provides a preselected passage. Researchers tackled
open-domain QA that presents a key challenge in natural language processing
(NLP). Open-domain QA considers a large text corpus such as Wikipedia pages
instead of a preselected passage for answering a given question. In this
context, the Natural Questions (NQ) dataset has presented a more
challenging problem. In fact, instead of providing one short passage for
each question, NQ gives an entire Wikipedia page which is significantly
longer than the passage provided in the other datasets.


An effective open-domain QA system must be able to successfully retrieve
the document and the passage on one hand, and comprehend the question
context to answer on the other. The current state-of-the-art of deep
learning-based research for open-domain QA is often complicated and consist
of mainly two components: (1) a passage retriever that selects a small
subset of passages from documents (e.g., Wikipedia pages), and then (2) a
machine comprehension that examines the retrieved passages to identify the
final answer. Several studies showed that passage retrieval impact and
impacts and can significantly improve question answering task.

Several elements are important for the passage retriever, such as question
and passage representation, similarity and attention mechanism between the
question and passages, passage ranking techniques, etc.

The REPARQA workshop is the first one that tackles the issue of passage
retrieval for open-domain QA. It aims to bring together experts from
industry, science, and academia to exchange ideas and discuss ongoing
research in open-domain QA and, more precisely, the passage retrieval
component. We encourage the description of a novel problem definition of
passage retrieval for open-domain QA and new datasets in this context.
Furthermore, we also encourage contributions to developing new techniques
for document retrieval for open-domain QA problems.

*Objective*

Traditional research on passage and document retrieval mainly focuses on
superficial similarities between the question and the passage (respectively
document), such as cosine similarity. The main distinguishing focus of this
workshop will be the use of deep neural networks and encoders for passage
retrievals, such as the use of encoders to represent questions and
passages, integrate attention mechanisms in the passage retrieval
framework, etc.


This workshop aims to discover the recent advances in passage retrieval for
open-domain QA and improve open-domain QA systems. Thereby, the REPARQ
workshop is an opportunity to inspire experts and researchers to share
theoretical and practical knowledge of the various aspects of QA systems,
to have focused discussions on the topic leading to converting the novel
ideas into future innovations.

*Topics of interests*

We invite the submission of original works that are related -- but are not
limited to -- the topics below:


   -

   Passage and document representation for open-domain QA
   -

   Attention mechanism for passage retrieval
   -

   Self-attention for passage and document retrieval
   -

   Passage and document retrieval based on unsupervised approaches
   -

   Passage and document ranking for open-domain QA
   -

   Reinforcement learning for passage retrieval
   -

   Passage and document retrieval using language models
   -

   Graph neural network for passage retrieval
   -

   Graph-based approaches using kernels
   -

   Ensemble approach for passage retrieval
   -

   Semantic understanding of passage and document content
   -

   Entity detection in questions and passages for context retrieval
   -

   Probabilistic graphical models
   -

   Parameterizations of specific passage retrieval systems for open-domain
   QA
   -

   Impact of passage retrieval performance on overall open-domain QA
   performance
   -

   Evaluation measures for assessing passage retrieval for open-domain QA

*Submission*

We invite two types of submissions, including original research papers (6-8
pages) as well as position papers (2-4 pages). Submissions must be
formatted according to the CIKM 2021 conference submissions formatting
guidelines. All papers will be peer-reviewed and assessed by the program
committee based on their novelty, technical quality, potential impact,
clarity, and reproducibility. Participants should publish their source
codes and include a link to their repository in the submitted papers. All
the papers are required to be submitted via the EasyChair
<https://easychair.org/conferences/?conf=reparqa2021> system.

At least one author of each accepted paper must register for the workshop,
in order to present the paper. For further instructions please refer to the
<https://www.google.com/url?q=https%3A%2F%2Fwww.cikm2021.org%2F&sa=D&sntz=1&usg=AFQjCNGXjrQEqNXJ4bUAhEdEwUH_ISR3YQ>CIKM
<https://www.google.com/url?q=https%3A%2F%2Fwww.cikm2021.org%2F&sa=D&sntz=1&usg=AFQjCNGXjrQEqNXJ4bUAhEdEwUH_ISR3YQ>
2021 <https://www.cikm2021.org/> page.

*Important dates*

   - July 15, 2021: Paper submission
   - August 15, 2021: Paper acceptance notification
   - November 1-5, 2021: Workshop dates will be flexible


Workshop Chairs

Rafika Boutalbi*, *University of Stuttgart, Germany

Rim Hantach*, *Engie, France

Mohamed Nadif*, *Universite de Paris, France