2019Q3 Reports: Tutorial Chairs

Tutorial chairs

• Preslav Nakov, Qatar Computing Research Institute, HBKU
• Alexis Palmer, University of North Texas

The tutorial call was coordinated between three conferences: ACL, NAACL, and EMNLP-ICJNLP. We looked for proposals in all areas of computational linguistics, broadly conceived to include related disciplines such as linguistics, speech, information retrieval, and multimodal processing.

We invited proposals for two types of tutorials: (a) cutting-edge and (b) introductory:

• Cutting-edge: tutorials that cover advances in newly emerging areas not previously covered in any ACL/EMNLP-IJCNLP/NAACL related tutorial.
• Introductory: tutorials that provide introductions to related fields that are potentially relevant to the computational linguistics community (e.g. bioinformatics, social media, human language processing, machine learning techniques).

As we recognize current problems of demographic imbalance in the field, we particularly encouraged submissions from members of under-represented groups in computational linguistics. Moreover, we made sure that tutorials are not "self-invited talks" and that they covered at least 50% other people's work. To check for this, we asked for the tutorial slides to be prepared one month prior to the date of the tutorial.

In addition to distributing the open call, we actively recruited tutorial proposals that we thought were likely to be of interest. The joint call received a total of 46 proposals, which were evaluated by the tutorial chairs from all three conferences.

First, each conference selected its top 8 tutorial proposals, with an emphasis on quality, diversity, and likely audience interest. Some overlaps in preferences led to a negotiation process in order to ensure that each conference would have a selection of tutorials with a good balance of introductory and cutting-edge tutorials, as well as balance between theoretical and applied tutorials (while keeping presenter location preferences in mind). We also had to account for the fact that some authors were affiliated with more than one successful tutorial proposal. To achieve this balance, we ended up accepting nine instead of eight tutorials for ACL. We are very thankful to the local organizers (Alessandro Lenci, Bernardo Magnini, and Simonetta Montemagni) for making that possible.

The following tutorials were accepted for ACL 2019:

• T1: Latent Structure Models for Natural Language Processing, by Andre F. T. Martins, Tsvetomile Mihaylova, Nikita Nangia, and Vlad Niculae
• T2: Graph-Based Meaning Representations: Design and Processing, by Alexander Koller, Stephan Oepen, and Weiwei Sun
• T3: Discourse Analysis and Its Applications, by Shafiq Joty, Giuseppe Carenini, Raymond Ng, and Gabriel Murray
• T4: Computational Analysis of Political Texts: Bridging Research Efforts Across Communities, by Goran Glavas, Federico Nanni, and Simone Paolo Ponzetto
• T5: Wikipedia as a Resource for Text Analysis and Retrieval, by Marius Pasca
• T6: Deep Bayesian Natural Language Processing, by Jen-Tzung Chien
• T7: Unsupervised Cross-Lingual Representation Learning, by Sebastian Ruder, Anders Sogaard, and Ivan Vulic
• T8: Advances in Argument Mining, by Katarzyna Budzynska and Chris Reed
• T9: Storytelling from Structured Data and Knowledge Graphs: An NLG Perspective, by Abhijit Mishra, Anirban Laha, Karthik Sankaranarayanan, Parag Jain, and Saravanan Krishnan

We would like to point to two difficulties. First, the process of balancing the tutorials across conferences was complex and required negotiation efforts between the tutorial chairs of the different conferences. Second, for those new to the tutorial process, there was some uncertainty about what steps needed to be taken and when. Fortunately, the group of six tutorial co-chairs included several who have done this before and were able to help the rest in the process, based on unwritten institutional memory. We suggest preparing a written guide for future tutorial chairs. This could be hosted on the ACL wiki and updated over time as appropriate.