default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID1st Insights 2020: Online
- Anna Rogers, João Sedoc, Anna Rumshisky:
Proceedings of the First Workshop on Insights from Negative Results in NLP, Insights 2020, Online, November 19, 2020. Association for Computational Linguistics 2020, ISBN 978-1-952148-66-8 - Anmol Nayak, Hariprasad Timmapathini, Karthikeyan Ponnalagu, Vijendran Gopalan Venkoparao:
Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words. 1-5 - Daria Dzendzik, Carl Vogel
, Jennifer Foster:
Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It's complicated! 6-14 - Samuel Louvan, Bernardo Magnini:
How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks. 15-21 - Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan, Rahul Gupta:
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks. 22-31 - Silvia Terragni, Debora Nozza, Elisabetta Fersini, Enza Messina:
Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models. 32-40 - Prasanna Parthasarathi, Sharan Narang, Arvind Neelakantan:
On Task-Level Dialogue Composition of Generative Transformer Model. 41-47 - Meghana Moorthy Bhat, Srinivasan Parthasarathy:
How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study. 48-53 - Ashwin Geet D'Sa, Irina Illina, Dominique Fohr, Dietrich Klakow, Dana Ruiter:
Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification. 54-59 - Catherine Finegan-Dollak, Ashish Verma:
Layout-Aware Text Representations Harm Clustering Documents by Type. 60-65 - Andrew Zupon, Faiz Rafique, Mihai Surdeanu:
An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios. 66-70 - Valentina Beretta, Sébastien Harispe, Katarina Boland, Luke Lo Seen, Konstantin Todorov, Andon Tchechmedjiev:
Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About? 71-75 - Zhengzhong Liang, Mihai Surdeanu:
Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules? 76-81 - William Huang, Haokun Liu, Samuel R. Bowman:
Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data. 82-87 - Alka Khurana, Vasudha Bhatnagar:
NMF Ensembles? Not for Text Summarization! 88-93 - Constantine Lignos, Marjan Kamyab:
If You Build Your Own NER Scorer, Non-replicable Results Will Come. 94-99 - Gaurav Arora, Chirag Jain, Manas Chaturvedi, Krupal Modi:
HINT3: Raising the bar for Intent Detection in the Wild. 100-105 - Yanai Elazar, Victoria Basmova, Shauli Ravfogel, Yoav Goldberg, Reut Tsarfaty:
The Extraordinary Failure of Complement Coercion Crowdsourcing. 106-116 - Steven R. Wilson, Walid Magdy, Barbara McGillivray, Gareth Tyson:
Embedding Structured Dictionary Entries. 117-125
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-11-28 23:53 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
