Description:
 
The advent of Deep Learning and the availability of large scale datasets has accelerated research on Natural Language Generation with a focus on newer tasks and better models. With such rapid progress, it is vital to assess the extent of scientific progress made and identify the areas/components that need improvement. To accomplish this in an automatic and reliable manner, the NLP community has actively pursued the development of automatic evaluation metrics. Especially in the last few years, there has been an increasing focus on evaluation metrics, with several criticisms of existing metrics and proposals for several new metrics. This tutorial presents the evolution of automatic evaluation metrics to their current state along with the emerging trends in this field by specifically addressing the following questions: (i) What makes NLG evaluation challenging? (ii) Why do we need automatic evaluation metrics? (iii) What are the existing automatic evaluation metrics and how can they be organised in a coherent taxonomy? (iv) What are the criticisms and shortcomings of existing metrics? (v) What are the possible future directions of research?
 
Speakers:
 
Mitesh M. Khapra and Ananya B. Sai

A Tutorial on Evaluation Metrics used in Natural Language Generation

Description:
 
In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for document-level representation learning. Additionally, our goal is to reveal new research opportunities to the audience, which will hopefully bring us closer to address existing challenges in this domain.
 
Speakers:
 
Iz Beltagy, Arman Cohan, Hannaneh Hajishirzi, Matthew E. Peters, Sewon Min

Beyond Paragraphs: NLP for Long Sequences

Description:
 
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems. There has seen a surge of interests in applying deep learning on graph techniques to NLP, and has achieved considerable success in many NLP tasks, ranging from classification tasks like sentence classification, semantic role labeling and relation extraction, to generation tasks like machine translation, question generation and summarization. Despite these successes, deep learning on graphs for NLP still face many challenges, including automatically transforming original text sequence data into highly graph-structured data, and effectively modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and graph data with multi-types in both nodes and edges. This tutorial will cover relevant and interesting topics on applying deep learning on graph techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic parsing). In addition, hands-on demonstration sessions will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library – Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.
 
Speakers:
 
Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li

Deep Learning on Graphs for Natural Language Processing

Description:
 
Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answering. Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. In this tutorial, we will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) intrinsic analysis, and ii) causation analysis. The former is a class of methods to analyze neurons with respect to a desired language concept or a task. The latter studies the role of neurons and input features in explaining the decisions made by the model. We will also discuss how interpretation methods and causation analysis can connect towards better interpretability of model prediction. Finally, we will walk you through various toolkits that facilitate fine-grained interpretation and causation analysis of neural models.
 
Speakers:
 
Hassan Sajjad, Narine Kokhlikyan, Fahim Dalvi, Nadir Durrani

Fine-grained Interpretation and Causation Analysis in Deep NLP Models

Website: http://www.netcopia.net/nlp4if/ 
 

Opening Remarks 
Invited Talk - Filippo Menczer, "4 Reasons Why Social Media Make Us Vulnerable to Manipulation" 
Session 1 
1) “Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models”, Galen Weld, Ellyn Ayton, Tim Althoff and Maria Glenski 
2) “Improving Hate Speech Type and Target Detection with Hateful Metaphor Features”, Jens Lemmens, Ilia Markov and Walter Daelemans 
3) “An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News”, Chaoyuan Zuo, Qi Zhang and Ritwik Banerjee 
Invited Talk - Margaret E. Roberts, "Resilience to Online Censorship" 
Session 2 
1) “Generalisability of Topic Models in Cross-corpora Abusive Language Detection”, Tulika Bose, Irina Illina and Dominique Fohr 
2) “Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News”, Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas and Rada Mihalcea 
3) “Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection”. Shaden Shaar, Firoj Alam, Giovanni Da San Martino, Alex Nikolov, Wajdi Zaghouani, Preslav Nakov and Anna Feldman 
Best Paper Award 
Poster Session 
Panel on “Misinformation, Disinformation, and Internet Freedom in the Time of the First Global Infodemic” 
Ending remarks 

Full program can be found at: http://www.netcopia.net/nlp4if/

Fourth Workshop on NLP for Internet Freedom (NLP4IF): Censorship, Disinformation, and Propaganda

Description:
 
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This tutorial, based on a forthcoming book, provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has, without exaggeration, revolutionized the fields of natural language processing (NLP), information retrieval (IR), and beyond. We provide a synthesis of existing work as a single point of entry for both researchers and practitioners. Our coverage is grouped into two categories:\ transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that perform ranking directly. Two themes pervade our treatment:\ techniques for handling long documents and techniques for addressing the tradeoff between effectiveness (result quality) and efficiency (query latency). Although transformer architectures and pretraining techniques are recent innovations, many aspects of their application are well understood. Nevertheless, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, we also attempt to prognosticate the future.
 
Speakers:
 
Andrew Yates, Rodrigo Nogueira, and Jimmy Lin

Pretrained Transformers for Text Ranking: BERT and Beyond

Website: https://sites.google.com/view/nlp4mc2021/home
 
Workshop Schedule: <a href="https://drive.google.com/file/d/14mfZnapSyCI0_IC0VwexraOiQg1A553G/preview" target="_blank"> Click Here</a>

The 2nd Workshop on NLP for Medical Conversations

Website: http://multicomp.cs.cmu.edu/naacl2021multimodalworkshop/
 

**Speakers:** 
• Kristen Grauman – University of Texas at Austin (USA) 
• Anind Dey - University of Washington 
• Emily Mower Provost – University of Michigan (USA) 
• Marcus Rohrbach – Facebook AI Research (USA) 
• Xin (Eric) Wang – University of California Santa Cruz (USA) 
 
**Schedule**
 
 Introduction and Remarks 
Invited Speaker #1 – Xin (Eric) Wang 
QA #1 
Invited Speaker #2 – Anind Dey 
QA #2 
Invited Speaker #3 – Emily Mower Provost 
 QA #3 
Invited Speaker #4 – Marcus Rohrbach 
QA #4 
Invited Speaker #5 – Kristen Grauman 
QA #5 
Poster Session

Third Workshop on Multimodal Artificial Intelligence (MAI-Workshop)

Description:
 
In this introductory tutorial, we present a portion of our six-year-long unique industry experience in efficient natural language data annotation via Crowdsourcing. We will make an introduction to data labeling via public crowdsourcing marketplaces, and will present the key components of efficient label collection that includes task design and decomposition, quality control, and annotator selection. This will be followed by a practical session, where participants address a real-world language resource production task, experiment with selecting settings for the labeling process, and launch their label collection project on one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session. We will present useful mathematical foundations, quality control techniques and tricks, and provide the attendees with an opportunity to discuss their own annotation ideas.
 
Speakers:
 
Alexey Drutsa, Dmitry Ustalov, Valentina Fedorova, Olga Megorskaya, Daria Baidakova

Crowdsourcing Natural Language Data at Scale:
A Hands-On Tutorial

Opening Ceremony

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

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NAACL 2021

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