Workshop at NAACL 2021

Mexico City, Mexico (June 11, 2021)

Workshop Description

The workshops in the TextGraphs series have published and promoted the synergy between the field of Graph Theory and Natural Language Processing. Besides traditional NLP applications like word sense disambiguation and semantic role labeling, and information extraction graph-based solutions nowadays also target new web-scale applications like information propagation in social networks, rumor proliferation, e-reputation, language dynamics learning, and future events prediction, to name a few.

Previous editions of the series can be found here.


As the NAACL-HLT 2021 conference hosts TextGraphs-15, registration at the host conference is required to attend our workshop: https://2021.naacl.org/registration/.

Please select the W15: TextGraphs-15: Graph-based Methods for Natural Language Processing workshop during the registration.

TextGraphs-15 will be held on June 11 at from 10am till 7pm PDT (UTC−7).

Join us on Underline: https://underline.io/events/122/sessions?eventSessionId=4305!


TextGraphs-15 proceedings are available on the ACL Anthology at https://www.aclweb.org/anthology/volumes/2021.textgraphs-1/.

Keynote Speakers

Jure Leskovec (Stanford University) https://cs.stanford.edu/~jure

Title (Tentative): Reasoning with Language and Knowledge Graphs

Abstract: Knowledge can be implicitly encoded in large language models pre-trained on unstructured text or explicitly represented in structured knowledge graphs, such as Freebase or ConceptNet, where entities are represented as nodes and relations between them as edges. Language models have a broad coverage of knowledge, but they do not perform well on structured reasoning tasks, KGs, on the other hand, are more suited for structured reasoning, but may lack coverage and be noisy. In this talk I will discuss recent advancements in combining the strengths of language models and knowledge graphs for common-sense question answering as well as complex logical reasoning over knowledge graphs.

Bio: Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining large social, information, and biological networks. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, marketing, and biomedicine. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University.

Laura Dietz (University of New Hempshire) https://www.cs.unh.edu/~dietz/

Title: ENT-Rank: Finding Relevant Entities through Text and Knowledge Graphs

Abstract: The development of knowledge graph construction methods and the availability of large general-purpose knowledge graphs (KGs) have led to several advances in information retrieval (IR). For example, entity linking and KGs provide additional useful information about text and search queries, giving rise to more accurate models of relevance. This KG-enabled retrieval approach sets a new standard for several IR benchmarks. However, the exploitation of relational information in IR proves difficult for various reasons, such as low extraction recall, sparsity of schemas, and biases in the extraction pipeline. We suggest an approach for using entity-centric information in textual context, called ENT-Rank. ENT-Rank integrates different relevance-indicators from text and neighboring entities with features of entities. The empirical evaluation within TREC CAR demonstrates that this approach significantly improves the identification of relevant entities (or concepts) in response to open domain queries. One application of ENT-Rank is to create topic-specific knowledge graphs. ENT-Rank also contributes towards our long-term vision of creating new Wikipedia-like articles for new topics through methods in retrieval, extraction, and summarization.

Bio: Laura Dietz is an Assistant Professor at the University of New Hampshire, where she leads the lab for text retrieval, extraction, machine learning and analytics (TREMA). She organizes a tutorial/workshop series on Utilizing Knowledge Graphs in Text-centric Retrieval (KG4IR) and coordinates the TREC Complex Answer Retrieval Track. She received an NSF CAREER Award for utilizing fine-grained knowledge annotations in text understanding and retrieval. Previously, she was a research scientist in the Data and Web Science group at Mannheim University, and a research scientist with Bruce Croft and Andrew McCallum at the Center for Intelligent Information Retrieval (CIIR) at UMass Amherst. She obtained her doctoral degree with a thesis on topic models for networked data from Max Planck Institute for Informatics, supervised by Tobias Scheffer and Gerhard Weikum.

Call for Papers

We invite submissions of up to eight (8) pages maximum, plus bibliography for long papers and four (4) pages, plus bibliography, for short papers.

The NAACL 2021 templates must be used; these are provided in LaTeX and also Microsoft Word format. Submissions will only be accepted in PDF format. Deviations from the provided templates will result in rejections without review.

Download the Word and LaTeX templates here: https://2021.naacl.org/calls/style-and-formatting/

Submit papers by the end of the deadline day (timezone is UTC-12) via our Softconf Submission Site: https://www.softconf.com/naacl2021/textgraphs2021

Shared Task

We are organizing a shared task before the workshop!

Many-hop multi-hop inference is challenging because there are often multiple ways of assembling a good explanation for a given question. This 2021 instantiation of the shared task focuses on the theme of determining relevance versus completeness in large multi-hop explanations. To this end, this year we include a very large dataset of approximately 250,000 expert-annotated relevancy ratings for facts ranked highly by baseline language models from previous years (e.g. BERT, RoBERTa).

Submissions using a variety of methods (graph-based or otherwise) are encouraged. Submissions that evaluate how well existing models designed on 2-hop multihop question answering datasets (e.g. HotPotQA, QASC, etc) perform at many-fact multi-hop explanation regeneration are welcome.

More information about the task held in TextGraphs-15 can be found here:

We welcome papers on the workshop topics even if you do not participate in the shared task.

Important Dates

  • Workshop Papers Due Date: March 22, 2021

  • Notification of Acceptance: April 15, 2021

  • Camera-ready Papers Due: April 26, 2021

  • Workshop Date: June 11, 2021

Workshop Topics

TextGraphs-15 invites submissions on (but not limited to) the following topics:

  • Graph-based and graph-supported machine learning and deep learning methods

    • Graph embeddings

    • Graph-based and graph-supported deep learning (e.g., graph-based recurrent and recursive networks)

    • Probabilistic graphical models and structure learning methods

    • Graph-based methods for reasoning and interpreting deep neural networks

    • Exploration of capabilities and limitations of graph-based methods being applied to neural networks,

    • Investigation of aspects of neural networks that are (not) susceptible to graph-based analysis

  • Graph-based methods for Information Retrieval, Information Extraction, and Text Mining

    • Graph-based methods for word sense disambiguation,

    • Graph-based representations for ontology learning,

    • Graph-based strategies for semantic relation identification,

    • Encoding semantic distances in graphs,

    • Graph-based techniques for text summarization, simplification, and paraphrasing

    • Graph-based techniques for document navigation and visualization,

    • Reranking with graphs,

    • Applications of label propagation algorithms, etc.

  • New graph-based methods for NLP applications

    • Random walk methods in graphs

    • Spectral graph clustering

    • Semi-supervised graph-based methods

    • Methods and analyses for statistical networks

    • Small world graphs

    • Dynamic graph representations

    • Topological and pretopological analysis of graphs

    • Graph kernels

  • Graph-based methods for applications on social networks

    • Rumor proliferation

    • E-reputation

    • Multiple identity detection

    • Language dynamics studies

    • Surveillance systems

  • Graph-based methods for NLP and Semantic Web

    • Representation learning methods for knowledge graphs (i.e., knowledge graph embedding)

    • Using graphs-based methods to populate ontologies using textual data

    • Inducing knowledge of ontologies into NLP applications using graphs

    • Merging ontologies with graph-based methods using NLP techniques

Program Committee

Željko Agić, Unity Technologies, Denmark

Ilseyar Alimova, Kazan Federal University, Russian Federation

Amir Bakarov, Behavox, Russian Federation

Sivaji Bandyopadhyay, Jadavpur University, NIT Silchar, India

Tomáš Brychcín, University of West Bohemia, Czech Republic

Ruben Cartuyvels, Catholic University of Leuven, Belgium

Flavio Massimiliano Cecchini, Università Cattolica del Sacro Cuore, Italy

Devansh Chandak, Indian Institute of Technology Bombay, India

Mihail Chernoskutov, IMM UB RAS, Russian Federation

Yew Ken Chia, Singapore University of Technology and Design, Singapore

Rajarshi Das, University of Massachusetts, Amherst, USA

Stefano Faralli, University of Rome Unitelma Sapienza, Italy

Deborah Ferreira, University of Manchester, UK

Michael Flor, Educational Testing Service, USA

Debanjan Ghosh, Educational Testing Service, USA

Goran Glavaš, University of Mannheim, Germany

Natalia Grabar, CNRS STL UMR8163, Université de Lille, France

Aayushee Gupta, International Institute of Information Technology, Bangalore, India

Binod Gyawali, Educational Testing Service, USA

Carlos Gómez-Rodríguez, Universidade da Coruña, Spain

Rima Hazra, Indian Institute of Technology, Kharagpur, India

Tomáš Hercig, University of West Bohemia, Czech Republic

Mohammad Javad Hosseini, University of Edinburgh, Informatics, UK

Dmitry Ilvovsky, National Research University Higher School of Economics, Russian Federation

Ming Jiang, University of Illinois at Urbana-Champaign, USA

Sammy Khalife, Ecole Polytechnique, France


Varun Madhavan, Indian Institute of Technology Kharagpur, India

Valentin Malykh, Huawei Noah's Ark Lab / Kazan Federal University, Russian Federation

Gabor Melli, OpenGov, USA

Alireza Mohammadshahi, IDIAP, Switzerland

Clayton Morrison, University of Arizona, USA

Animesh Mukherjee, IIT Kharagpur, India

Matthew Mulholland, Educational Testing Service, USA

Giannis Nikolentzos, Ecole Polytechnique, France

Enrique Noriega-Atala, The University of Arizona, USA

Damien Nouvel, INALCO ERTIM, France

Aditya Girish Pawate, Indian Institute of Technology, Kharagpur, India

Jan Wira Gotama Putra, Tokyo Institute of Technology, Japan

Zimeng Qiu, Amazon Alexa AI, USA

Leonardo F. R. Ribeiro, TU Darmstadt, Germany

Michael Richter, Leipzig University, Germany

Stephen Roller, Facebook AI Research, USA

Minoru Sasaki, Ibaraki University, Japan

Viktor Schlegel, University of Manchester, UK

Rebecca Sharp, University of Arizona, USA

Artem Shelmanov, Skolkovo Institute of Science and Technology, Russian Federation

Konstantinos Skianis, BLUAI, Greece

Mark Steedman, University of Edinburgh, UK

Saatviga Sudhahar, Healx, UK

Mokanarangan Thayaparan, University of Manchester, UK

Antoine Tixier, Ecole Polytechnique, Palaiseau, France, France

Nicolas Turenne, BNU HKBU United International College (UIC), China

Adrian Ulges, RheinMain University of Applied Sciences, Germany

Vaibhav Vaibhav, Apple, USA

Serena Villata, Université Côte d’Azur, CNRS, Inria, I3S, France

Anssi Yli-Jyrä, University of Helsinki, Finland

Xiang Zhao, National University of Defense Technology, China

Workshop Organizers

Alexander Panchenko, Skoltech

Fragkiskos D. Malliaros, CentraleSupélec, University of Paris-Saclay

Varvara Logacheva, Skoltech

Abhik Jana, University of Hamburg

Dmitry Ustalov, Yandex

Peter Jansen, School of Information, University of Arizona

Shared Task Organizers

Mokanarangan Thayaparan, University of Manchester

Marco Valentino, University of Manchester

Peter Jansen, School of Information, University of Arizona

Dmitry Ustalov, Yandex


Please direct all questions and inquiries to our official e-mail address (textgraphsOC@gmail.com) or contact any of the organizers via their individual emails.

Connect with us on social media:


TextGraphs-15 will be held on June 11 at from 10am till 7pm PDT (UTC−7).

Workshop schedule is present in the table below.