The first iteration of Insights attracted 35 submissions and 11 presentation requests from authors of papers accepted to “Findings of EMNLP”. We accepted 18 research papers (51.4\% acceptance rate) and granted 5 presentation requests.

Accepted papers

• Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words
  Anmol Nayak, hariprasad timmapathini, karthikeyan Ponnalagu and Vijendran Gopalan Venkoparao

• Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It’s complicated!
  Daria Dzendzik, Carl Vogel and Jennifer Foster

• How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks
  Samuel Louvan and Bernardo Magnini

• Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks
  Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan and Rahul Gupta

• Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models
  Silvia Terragni, Debora Nozza, Elisabetta Fersini and Messina Enza

• On Task-Level Dialogue Composition of Generative Transformer Model
  Prasanna Parthasarathi, Sharan Narang and Arvind Neelakantan

• How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study
  Meghana Moorthy Bhat and Srinivasan Parthasarathy

• Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification
  Ashwin Geet D’Sa, Irina Illina, Dominique Fohr and Dietrich Klakow

• Layout-Aware Text Representations Harm Clustering Documents by Type
  Catherine Finegan-Dollak and Ashish Verma

• An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios
  Andrew Zupon, Faiz Rafique and Mihai Surdeanu

• Can Multimodal Embeddings Tell Us What Fact-checked Claims Are About?
  Valentina Beretta, Sébastien Harispe, Katarina Boland, Luke Lo Seen, Konstantin Todorov and Andon Tchechmedjiev

• Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?
  Zhengzhong Liang and Mihai Surdeanu

• Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data
  William Huang, Haokun Liu and Samuel R. Bowman

• NMF Ensembles? Not for Text Summarization!
  Alka Khurana and Vasudha Bhatnagar

• If You Build Your Own NER Scorer, Non-replicable Results Will Come
  Constantine Lignos and Marjan Kamyab

• HINT3: Raising the bar for Intent Detection in the Wild
  Gaurav Arora, Chirag Jain, Manas Chaturvedi and Krupal Modi

• Embedding Structured Dictionary Entries
  Steven Wilson, Walid Magdy, Barbara McGillivray and Gareth Tyson

• The Extraordinary Failure of Complement Coercion Crowdsourcing
  Yanai Elazar, Victoria Basmov, Shauli Ravfogel, Yoav Goldberg and Reut Tsarfat

Accepted non-archival papers

• How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?
  Shayne Longpre, Yu Wang and Christopher DuBois

• Effects of Naturalistic Variation in Goal-Oriented Dialog
  Jatin Ganhotra, Robert Moore, Sachindra Joshi and Kahini Wadhawan

• Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
  Alvin Chan, Yi Tay, Yew-Soon Ong and Aston Zhang

• WER we are and WER we think we are
  Piotr Szymański, Piotr Żelasko, Mikolaj Morzy, Adrian Szymczak, Marzena Żyła-Hoppe, Joanna Banaszczak, Lukasz Augustyniak, Jan Mizgajski and Yishay Carmiel

• What do we expect from Multiple-choice QA Systems?
  Krunal Shah, Nitish Gupta and Dan Roth