Ehud Reiter and Craig Thomson join annual gathering of the world's foremost natural language generation experts

MORRISTOWN, NJ / ACCESSWIRE / October 27, 2019 / today announced that its Chief Scientist, and University of Aberdeen Professor, Ehud Reiter, and Applied Research Scientist, Craig Thomson, will take part in educational workshops at the (INLG 2019) taking place at the National Museum of Emerging Science and Innovation in Tokyo, Japan, October 29 - November 1, 2019.

Organized by the Artificial Intelligence Research Center of Japan (AIRC, AIST), INLG 2019 will feature a combination of invited lectures, regular oral and poster presentations, and highly interactive educational workshops.

WORKSHOP BY EHUD REITER

In addition to his role as Arria NLG Chief Scientist, Reiter is an esteemed professor of computing science at the University of Aberdeen. He is among industry thought leaders who, as program committee members for the half-day workshop, are directing, "

WHAT:

WHO: Organizers: Alejandro Catalá and Jose M. Alonso
Featuring: Ehud Reiter, Chief Scientist, Arria NLG, and Professor, University of Aberdeen

WHEN: Tuesday, October 29, 2019, 10:00 AM - 1:00 PM Local Time

WHERE: National Museum of Emerging Science and Innovation (Tokyo, Japan)

This workshop will focus on the automatic generation of interactive explanations in natural language and the complementary value NLG brings to data visuals and business intelligence tools. Reiter will share his expertise on four specific challenges to successfully generating good explanations of AI reasoning: evaluation; vague language; narrative; and data quality.

"This year we expect to see progress on neural-network and deep-learning techniques in NLG, particularly with the reduction of hallucination, which is when black-box systems inexplicably output incorrect content," said Reiter. "We also look forward to reviewing a lot of exciting work on better techniques for evaluating NLG systems."

WORKSHOP WITH CRAIG THOMSON

Thomson joined Arria on a doctoral studentship from the Engineering and Physical Sciences Research Council. Thomson will take part in the workshop, "". In addition, he will present findings from his current research, highlighting the many ways that machine learning can augment Arria NLG technology throughout the event.

WHAT:

WHO: Organizers: Anusha Balakrishnan, Vera Demberg, Chandra Khatri, Abhinav Rastogi, Donia Scott, Marilyn Walker, Michael White

WHEN: Wednesday, October 30, 2019, 10:00 AM - 1:00 PM and 2:30 PM - 7:00 PM Local Time

WHERE: National Museum of Emerging Science and InnovationTokyo, Japan

These sessions are designed to stimulate further research on enhancing quality in Neural (NNLG) in terms of discourse coherence and cohesion, as well as ways to make NNLG models easier to control.

Topics to be covered include: the limits of current end-to-end NNLG with respect to sentence planning and discourse structure; methods for improving discourse coherence and cohesion in NNLG; methods for control and interpretability of NNLG; and better methods for evaluating discourse coherence and cohesion in NNLG.

About INLG

INLG 2019 is organized under the auspices of the Special Interest Group on Natural Language Generation (SIGGEN) of the Association for Computational Linguistics (ACL) and the International Speech Communication Association (ISCA). This year's INLG conference will be an exciting combination of invited lectures, and regular oral and poster presentations.

About Arria NLG

Arria NLG is the global leader in the field of (NLG), a form of artificial intelligence specializing in extracting insights from complex data sources and communicating that information in natural language (i.e. as if written or spoken by a human). The company owns, develops and licenses its technology through its Arria NLG Studio Platform. Arria NLG Studio empowers writers and subject matter experts to create an effectively unlimited number of sophisticated narratives based on structured data sets. Arria's simple web-based interface contains a set of easy-to-understand functions that correspond to the array of analytical, linguistic, and stylistic decisions a human writer makes when composing thoughtful narrative. For additional information, please visit .

CONTACT:

SOURCE: Arria NLG