Committee that developed this policy:
* Bonnie Webber
* Christy Doran
* Huang Xuanjing
* Joel Tetreault
* Jonathan Kummerfeld [chair]
* Yusuke Miyao [chair]

Edits were made by Jonathan Kummerfeld based on discussion with the ACL Executive. Feedback was solicited from the community and informed further improvements.

=== Anonymity ===

We discussed requiring authors to be anonymous to avoid potential a source of bias, but realised it was (1) not easy to do with camera-ready versions of papers, and (2) would be difficult to maintain while also handling conflict-of-interests on the best paper committee (COIs will be more likely than in the past because more papers are being considered). Given those concerns, we have made it optional.

=== Choice of terms ===

The committee had mixed opinions on the term 'best'. On one hand, it is common/standard and expected, on the other hand, it implies a metric with a clear ranking of papers, which we do not have. After discussing a range of alternatives we returned to 'best' because it is such a well established term. Several options for the second category were discussed, including 'noteworthy', 'honourable mention', and 'outstanding'. The final proposal uses 'outstanding' as it
is a recognisable term.

=== Choice of numbers ===

* "Typically no more than 6" best papers. This value was chosen based on what we thought could be included in a single plenary session. However, the language is intentionally flexible.
* "1.5–2.5% of accepted papers" receive the outstanding paper award. This range was chosen to balance prestige (which favors a lower number) and the goal to recognize more work (which favors a higher number).
* For context, the graph below shows historical trends in awards at ACL going back to the very first year a best paper award was announced (note, these values include all award types together in the count for each year):

[[File: papers receiving awards at ACL.png|800px]]

=== Several ideas came up that are worth considering in the future: ===

* Dissertation Awards. These exist in other communities (e.g. AAAI) and are intended to recognise a body of work. We did not include them as they are outside of the conference awards process. They would need a separate process that could, for example, be conducted by each organisation / society separately. We also discussed awards for thesis proposals, which would be suitable for the Student Research Workshop.
* Journal Paper Awards. Other communities have these (e.g. ISCA for papers in CSL). We discussed including them here by saying CL and TACL papers presented at a conference are eligible for awards, but then the awards committee needs to compare quite different papers (e.g. a 30+ page journal article and a 4 page short conference paper). The approach used elsewhere is that each journal chooses a 'best paper', but that is then outside the scope of this proposal regarding conferences.
* Requirements for papers to be well-presented. We discussed the idea that papers that receive awards should be examples of well-presented ideas that students can look to for understanding how to write a good paper. On the other hand, it would seem unfair to not reward an innovative idea just because the author is inexperienced and did not present it perfectly. We tried to come up with language to balance these factors, but decided there was no simple solution, so this requirement was dropped.
* Advice/guidance for reviewers on when to select a paper for an award. This is a good idea that is outside the scope of this policy.

ACL Conference Awards Policy

2025-04-06T07:12:39Z

Yukiar: ACL conference award policy update on March 2025

Past tutorials

2024-07-25T03:56:10Z

Yukiar: 2021 Tutorials

Past tutorials

2024-07-25T03:30:55Z

Yukiar: /* 2020 tutorials */

This page belongs to the [[tutorial chair handbook]]. It summarizes data on tutorials which took place at some recent ACL, EACL, NAACL, EMNLP and COLING conferences.

==2020 tutorials==

{| border="1" cellspacing="0" cellpadding="5"
|'''Title'''
|'''Trainers'''
|'''Conference'''
|'''Conference link'''
|'''Virtual conference link'''
|'''ACL Anthology link'''
|-
|Interpretability and Analysis in Neural NLP
|Yonatan Belinkov, Sebastian Gehrmann and Ellie Pavlick
|ACL 2020
|[https://acl2020.org/program/tutorials/#t1-interpretability-and-analysis-in-neural-nlp-cutting-edge-]
|[https://virtual.acl2020.org/tutorial_T1.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.1/]
|-
|Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web
|Xin Luna Dong, Hannaneh Hajishirzi, Colin Lockard and Prashant Shiralkar
|ACL 2020
|[https://acl2020.org/program/tutorials/#t2-multi-modal-information-extraction-from-text-semi-structured-and-tabular-data-on-the-web-cutting-edge-]
|[https://virtual.acl2020.org/tutorial_T2.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.6/]
|-
|Reviewing Natural Language Processing Research
|Kevin Cohen, Karën Fort, Margot Mieskes and Aurélie Névéol
|ACL 2020
|[https://acl2020.org/program/tutorials/#t3-reviewing-natural-language-processing-research-introductory-]
|[https://virtual.acl2020.org/tutorial_T3.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.4/]
|-
|Stylized Text Generation: Approaches and Applications
|Lili Mou and Olga Vechtomova
|ACL 2020
|[https://acl2020.org/program/tutorials/#t4-stylized-text-generation-approaches-and-applications-cutting-edge-]
|[https://virtual.acl2020.org/tutorial_T4.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.5/]
|-
|Achieving Common Ground in Multi-modal Dialogue
|Malihe Alikhani and Matthew Stone
|ACL 2020
|[https://acl2020.org/program/tutorials/#t5-achieving-common-ground-in-multi-modal-dialogue-cutting-edge-]
|[https://virtual.acl2020.org/tutorial_T5.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.3/]
|-
|Commonsense Reasoning for Natural Language Processing
|Maarten Sap, Vered Shwartz, Antoine Bosselut, Yejin Choi and Dan Roth
|ACL 2020
|[https://acl2020.org/program/tutorials/#t6-commonsense-reasoning-for-natural-language-processing-introductory-]
|[https://virtual.acl2020.org/tutorial_T6.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.7/]
|-
|Integrating Ethics into the NLP Curriculum
|Emily M. Bender, Dirk Hovy and Alexandra Schofield
|ACL 2020
|[https://acl2020.org/program/tutorials/#t7-integrating-ethics-into-the-nlp-curriculum-introductory-]
|[https://virtual.acl2020.org/tutorial_T7.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.2/]
|-
|Open-Domain Question Answering
|Danqi Chen and Scott Wen-tau Yih
|ACL 2020
|[https://acl2020.org/program/tutorials/#t8-open-domain-question-answering-cutting-edge-]
|[https://virtual.acl2020.org/tutorial_T8.html]
|[https://www.aclweb.org/anthology/2020.acl-tutorials.8/]
|-
|Natural Language Processing in Financial Technology Applications
|Hsin-Hsi Chen, Hen-Hsen Huang and Chung-Chi Chen
|AACL 2020
|[https://aacl2020.org/program/tutorial_schedule/]
|
|
|-
|NLP for Healthcare in the Absence of a Healthcare Dataset
|Sarvnaz Karimi and Aditya Joshi
|AACL 2020
|[https://aacl2020.org/program/tutorial_schedule/]
|
|
|-
|Self-Supervised Deep Learning for NLP
|William Yang Wang and Xin Wang
|AACL 2020
|[https://aacl2020.org/program/tutorial_schedule/]
|
|
|-
|Explainability for Natural Language Processing
|Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
|AACL 2020
|[https://aacl2020.org/program/tutorial_schedule/]
|
|
|-
|A Hitchhikers guide to using Transformers for multiple scenarios and languages
|Said Bleik, Miguel Fierro, Hong Lu, Daisy Deng, Yijing Chen, Heather Spetalnick, Tao Wu and Sharat Chikkerur
|AACL 2020
|[https://aacl2020.org/program/tutorial_schedule/]
|
|
|-
|Advances in Debating Technologies: Building AI That Can Debate Humans
|Roy Bar-Haim, Yonatan Bilu, Liat Ein-Dor and Noam Slonim
|AACL 2020
|[https://aacl2020.org/program/tutorial_schedule/]
|
|
|-
|Machine Reasoning: Technology, Dilemma and Future
|Nan Duan, Duyu Tang, Ming Zhou
|EMNLP 2020
|[https://2020.emnlp.org/tutorials]
|[https://virtual.2020.emnlp.org/tutorial_T4.html]
|| [https://aclanthology.org/2020.emnlp-tutorials.1/]
|-
|Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era
|Preslav Nakov, Giovanni Da San Martino
|EMNLP 2020
|[https://2020.emnlp.org/tutorials]
|[https://virtual.2020.emnlp.org/tutorial_T2.html]
|[https://aclanthology.org/2020.emnlp-tutorials.2/]
|-
|Interpreting Predictions of NLP Models
|Eric Wallace, Matt Gardner, Sameer Singh
|EMNLP 2020
|[https://2020.emnlp.org/tutorials]
|[https://virtual.2020.emnlp.org/tutorial_T1.html]
|[https://aclanthology.org/2020.emnlp-tutorials.3/]
|-
|High Performance Natural Language Processing
|Gabriel Ilharco, Cesar Ilharco, Iulia Turc, Tim Dettmers, Felipe Ferreira, Kenton Lee
|EMNLP 2020
|[https://2020.emnlp.org/tutorials]
|[https://virtual.2020.emnlp.org/tutorial_T3.html]
|[https://aclanthology.org/2020.emnlp-tutorials.4/]
|-
|Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks
|Parisa Kordjamshidi, James Pustejovsky, Marie-Francine Moens
|EMNLP 2020
|[https://2020.emnlp.org/tutorials]
|[https://virtual.2020.emnlp.org/tutorial_T5.html]
|[https://aclanthology.org/2020.emnlp-tutorials.5/]
|-
|Simultaneous Translation
|Liang Huang, Colin Cherry, Mingbo Ma, Naveen Arivazhagan, Zhongjun He
|EMNLP 2020
|[https://2020.emnlp.org/tutorials]
|[https://virtual.2020.emnlp.org/tutorial_T6.html]
|[https://aclanthology.org/2020.emnlp-tutorials.6/]
|-
|The Amazing World of Neural Language Generation
|Yangfeng Ji, Antoine Bosselut, Thomas Wolf, Asli Celikyilmaz
|EMNLP 2020
|[https://2020.emnlp.org/tutorials]
|[https://virtual.2020.emnlp.org/tutorial_T7.html]
|[https://aclanthology.org/2020.emnlp-tutorials.7/]
|-
|Cross-lingual Semantic Representation for NLP with UCCA
|Omri Abend, Dotan Dvir, Daniel Hershcovich, Jakob Prange, Nathan Schneider
|COLING 2020
|[https://coling2020.org/pages/tutorials]
|
|[https://aclanthology.org/2020.coling-tutorials.1/]
|-
|Embeddings in Natural Language Processing
|Jose Camacho-Collados, Mohammad Taher Pilehvar
|COLING 2020
|[https://coling2020.org/pages/tutorials]
|
|[https://aclanthology.org/2020.coling-tutorials.2/]
|-
|Multilingual Neural Machine Translation
|Raj Dabre, Chenhui Chu, Anoop Kunchukuttan
|COLING 2020
|[https://coling2020.org/pages/tutorials]
|
|[https://aclanthology.org/2020.coling-tutorials.3/]
|-
|Detection and Resolution of Rumors and Misinformation with NLP
|Leon Derczynski, Arkaitz Zubiaga
|COLING 2020
|[https://coling2020.org/pages/tutorials]
|
|[https://aclanthology.org/2020.coling-tutorials.4/]
|-
|A guide to the dataset explosion in QA, NLI, and commonsense reasoning
|Anna Rogers, Anna Rumshisky
|COLING 2020
|[https://coling2020.org/pages/tutorials]
|
|[https://aclanthology.org/2020.coling-tutorials.5/]
|-
|A Crash Course in Automatic Grammatical Error Correction
|Roman Grundkiewicz, Christopher Bryant, Mariano Felice
|COLING 2020
|[https://coling2020.org/pages/tutorials]
|
|[https://aclanthology.org/2020.coling-tutorials.5/]
|-
|Endangered Languages meet Modern NLP
|Antonios Anastasopoulos, Christopher Cox, Graham Neubig, Hilaria Cruz
|COLING 2020
|[https://coling2020.org/pages/tutorials]
|
|[https://aclanthology.org/2020.coling-tutorials.6/]
|-
|}

==2019 tutorials==

{| border="1" cellspacing="0" cellpadding="5"
|'''Title'''
|'''Trainers'''
|'''Conference'''
|'''Conference link'''
|'''ACL Anthology link'''
|-
|Latent Structure Models for Natural Language Processing
|André F. T. Martins, Tsvetomila Mihaylova, Nikita Nangia and Vlad Niculae
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T1]
|[https://www.aclweb.org/anthology/P19-4001/]
|-
|Graph-Based Meaning Representations: Design and Processing
|Alexander Koller, Stephan Oepen and Weiwei Sun
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T2]
|[https://www.aclweb.org/anthology/P19-4002/]
|-
|Discourse Analysis and Its Applications
|Shafiq Joty, Giuseppe Carenini, Raymond Ng and Gabriel Murray
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T3]
|[https://www.aclweb.org/anthology/P19-4003/]
|-
|Computational Analysis of Political Texts: Bridging Research Efforts Across Communities
|Goran Glavaš, Federico Nanni and Simone Paolo Ponzetto
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T4]
|[https://www.aclweb.org/anthology/P19-4004/]
|-
|Wikipedia as a Resource for Text Analysis and Retrieval
|Marius Pasca
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T5]
|[https://www.aclweb.org/anthology/P19-4005/]
|-
|Deep Bayesian Natural Language Processing
|Jen-Tzung Chien
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T6]
|[https://www.aclweb.org/anthology/P19-4006/]
|-
|Unsupervised Cross-Lingual Representation Learning
|Sebastian Ruder, Anders Søgaard and Ivan Vulić
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T7]
|[https://www.aclweb.org/anthology/P19-4007/]
|-
|Advances in Argument Mining
|Katarzyna Budzynska and Chris Reed
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T8]
|[https://www.aclweb.org/anthology/P19-4008/]
|-
|Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective
|Abhijit Mishra, Anirban Laha, Karthik Sankaranarayanan, Parag Jain and Saravanan Krishnan
|ACL 2019
|[http://www.acl2019.org/EN/tutorials.xhtml#T9]
|[https://www.aclweb.org/anthology/P19-4009/]
|-
|Deep Adversarial Learning for NLP
|William Yang Wang, Sameer Singh and Jiwei Li
|NAACL 2019
|[https://naacl2019.org/program/tutorials/#t1-deep-adversarial-learning-for-nlp]
|[https://aclweb.org/anthology/N19-5001/]
|-
|Deep Learning for Natural Language Inference
|Samuel Bowman and Xiaodan Zhu
|NAACL 2019
|[https://naacl2019.org/program/tutorials/#t2-deep-learning-for-natural-language-inference]
|[https://aclweb.org/anthology/N19-5002/]
|-
|Measuring and Modeling Language Change
|Jacob Eisenstein
|NAACL 2019
|[https://naacl2019.org/program/tutorials/#t3-measuring-and-modeling-language-change]
|[https://aclweb.org/anthology/N19-5003/]
|-
|Transfer Learning in Natural Language Processing
|Sebastian Ruder, Matthew Peters, Swabha Swayamdipta and Thomas Wolf
|NAACL 2019
|[https://naacl2019.org/program/tutorials/#t4-transfer-learning-in-natural-language-processing]
|[https://aclweb.org/anthology/N19-5004/]
|-
|Language Learning and Processing in People and Machines
|Aida Nematzadeh, Richard Futrell and Roger Levy
|NAACL 2019
|[https://naacl2019.org/program/tutorials/#t5-language-learning-and-processing-in-people-and-machines]
|[https://aclweb.org/anthology/N19-5005/]
|-
|Applications of Natural Language Processing in Clinical Research and Practice
|Yanshan Wang, Ahmad Tafti, Sunghwan Sohn and Rui Zhang
|NAACL 2019
|[https://naacl2019.org/program/tutorials/#t6-applications-of-natural-language-processing-in-clinical-research-and-practice]
|[https://aclweb.org/anthology/N19-5006/]
|}

==2018 tutorials==

{| border="1" cellspacing="0" cellpadding="5"
|'''Title'''
|'''Trainers'''
|'''Conference'''
|'''Conference link'''
|'''ACL Anthology link'''
|-
|Joint models for NLP
|Yue Zhang
|EMNLP 2018
|[https://emnlp2018.org/program/tutorials/]
|
|-
|Graph Formalisms for Meaning Representations
|Adam Lopez and Sorcha Gilroy
|EMNLP 2018
|[https://emnlp2018.org/program/tutorials/]
|
|-
|Writing Code for NLP Research
|Matt Gardner, Mark Neumann, Joel Grus, and Nicholas Lourie
|EMNLP 2018
|[https://emnlp2018.org/program/tutorials/]
|
|-
|Deep Latent Variable Models of Natural Language
|Alexander Rush, Yoon Kim, and Sam Wiseman
|EMNLP 2018
|[https://emnlp2018.org/program/tutorials/]
|
|-
|Standardized Tests as benchmarks for Artificial Intelligence
|Mrinmaya Sachan, Minjoon Seo, Hannaneh Hajishirzi, and Eric Xing
|EMNLP 2018
|[https://emnlp2018.org/program/tutorials/]
|
|-
|Deep Chit-Chat: Deep Learning for ChatBots
|Wei Wu and Rui Yan
|EMNLP 2018
|[https://emnlp2018.org/program/tutorials/]
|
|-
|100 Things You Always Wanted to Know about Semantics & Pragmatics But Were Afraid to Ask
|Emily M. Bender
|ACL 2018
|[https://acl2018.org/tutorials/#100-things-you-always-wante]
|[https://aclweb.org/anthology/P18-5001/]
|-
|Neural Approaches to Conversational AI
|Jianfeng Gao, Michel Galley and Lihong Li
|ACL 2018
|[https://acl2018.org/tutorials/#neural-approaches-to-conver]
|[https://aclweb.org/anthology/P18-5002/]
|-
|Variational Inference and Deep Generative Models
|Wilker Aziz and Philip Schulz
|ACL 2018
|[https://acl2018.org/tutorials/#variational-inference-and-d]
|[https://aclweb.org/anthology/P18-5003/]
|-
|Connecting Language and Vision to Actions
|Peter Anderson, Abhishek Das and Qi Wu
|ACL 2018
|[https://acl2018.org/tutorials/#connecting-language-and-vis]
|[https://aclweb.org/anthology/P18-5004/]
|-
|Beyond Multiword Expressions: Processing Idioms and Metaphors
|Valia Kordoni
|ACL 2018
|[https://acl2018.org/tutorials/#beyond-multiword-expression]
|[https://aclweb.org/anthology/P18-5005/]
|-
|Neural Semantic Parsing
|Luke Zettlemoyer, Matt Gardner, Pradeep Dasigi, Srinivasan Iyer and Alane Suhr
|ACL 2018
|[https://acl2018.org/tutorials/#neural-semantic-parsing]
|[https://aclweb.org/anthology/P18-5006/]
|-
|Deep Reinforcement Learning for NLP
|William Yang Wang, Jiwei Li and Xiaodong He
|ACL 2018
|[https://acl2018.org/tutorials/#deep-reinforcement-learning]
|[https://aclweb.org/anthology/P18-5007/]
|-
|Multi-lingual Entity Discovery and Linking
|Avirup Sil, Heng Ji, Dan Roth and Silviu-Petru Cucerzan
|ACL 2018
|[https://acl2018.org/tutorials/#multi-lingual-entity-discov]
|[https://aclweb.org/anthology/P18-5008/]
|-
|Modelling Natural Language, Programs, and their Intersection
|Graham Neubig and Miltiadis Allamanis
|NAACL 2018
|
|[https://aclweb.org/anthology/N18-6001/]
|-
|Deep Learning Approaches to Text Production
|Claire Gardent and Shashi Narayan
|NAACL 2018
|
|[https://aclweb.org/anthology/N18-6002/]
|-
|Scalable Construction and Reasoning of Massive Knowledge Bases
|Xiang Ren, Nanyun Peng and William Yang Wang
|NAACL 2018
|
|[https://aclweb.org/anthology/N18-6003/]
|-
|The interplay between lexical resources and Natural Language Processing
|Jose Camacho-Collados, Luis Espinosa Anke and Mohammad Taher Pilehvar
|NAACL 2018
|
|[https://aclweb.org/anthology/N18-6004/]
|-
|Socially Responsible NLP
|Yulia Tsvetkov, Vinodkumar Prabhakaran and Rob Voigt
|NAACL 2018
|
|[https://aclweb.org/anthology/N18-6005/]
|-
|Deep Learning for Conversational AI
|Pei-Hao Su, Nikola Mrkšić, Iñigo Casanueva, Ivan Vulić
|NAACL 2018
|
|[https://aclweb.org/anthology/N18-6006/]
|-
|NLP for Conversations: Sentiment, Summarization, and Group Dynamics
|Gabriel Murray, Giuseppe Carenini and Shafiq Joty
|COLING 2018
|[https://sites.google.com/view/nlpforconversations]
|[https://aclweb.org/anthology/C18-3001/]
|-
|Practical Parsing for Downstream Applications
|Daniel Dakota and Sandra Kübler
|COLING 2018
|[http://cl.indiana.edu/colingParsing18/]
|[https://aclweb.org/anthology/C18-3002/]
|-
|Frame Semantics across Languages: Towards a Multilingual FrameNet
|Collin Baker, Michael Ellsworth, Miriam R L Petruck and Swabha Swayamdipta
|COLING 2018
|[https://framenet.icsi.berkeley.edu/fndrupal/node/5552/]
|[https://aclweb.org/anthology/C18-3003/]
|-
|Deep Bayesian Learning and Understanding
|Jen-Tzung Chien
|COLING 2018
|[http://chien.cm.nctu.edu.tw/home/coling/]
|[https://aclweb.org/anthology/C18-3004/]
|-
|Data-Driven Text Simplification
|Sanja Štajner and Horacio Saggion
|COLING 2018
|[http://taln.upf.edu/pages/coling2018simplification/]
|[https://aclweb.org/anthology/C18-3005/]
|-
|Deep Learning for Dialogue Systems
|Yun-Nung Chen, Asli Celikyilmaz and Dilek Hakkani-Tur
|COLING 2018
|[http://deepdialogue.miulab.tw/]
|[https://aclweb.org/anthology/C18-3006/]
|}

==2017 tutorials==

EMNLP 2017 website is no longer available. There are no traces of the AMNLP 2017 tutorials, except this [https://www.facebook.com/emnlp2017/posts/well-have-7-tutorials-at-emnlp2017-concepthier-sarcasm-graphrep-srl-memaugnn-str/1860468284191872/ Facebook post].

{| border="1" cellspacing="0" cellpadding="5"
|'''Title'''
|'''Trainers'''
|'''Conference'''
|'''Conference link'''
|'''ACL Anthology link'''
|-
|Universal Dependencies
|Joakim Nivre, Daniel Zeman, Filip Ginter, and Francis Tyers
|EACL 2017
|[http://universaldependencies.org/eacl17tutorial/]
|
|-
|Practical Neural Machine Translation
|Rico Sennrich and Barry Haddow
|EACL 2017
|[http://www.statmt.org/eacl2017/practical-nmt.pdf]
|
|-
|Imitation learning for structured prediction in natural language processing
|Andreas Vlachos, Gerasimos Lampouras and Sebastian Riedel
|EACL 2017
|[https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/]
|
|-
|Word Vector Space Specialisation
|Ivan Vulić, Nikola Mrkšić, and Mohammad Taher Pilehvar
|EACL 2017
|[http://people.ds.cam.ac.uk/iv250/tutorial/wv-tutorial.pdf]
|
|-
|Integer Linear Programming formulations in Natural Language Processing
|Dan Roth and Vivek Srikumar
|EACL 2017
|[https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/]
|
|-
|Building Multimodal Simulations for Natural Language
|James Pustejovsky and Nikhil Krishnaswamy
|EACL 2017
|[http://www.voxicon.net]
|
|-
|Natural Language Processing for Precision Medicine
|Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih
|ACL 2017
|[http://acl2017.org/tutorials/]
|[https://aclweb.org/anthology/P17-5001/]
|-
|Multimodal Machine Learning
|Louis-Philippe Morency and Tadas Baltrusaitis
|ACL 2017
|[http://acl2017.org/tutorials/]
|[https://aclweb.org/anthology/P17-5002/]
|-
|Deep Learning for Semantic Composition
|Xiaodan Zhu and Edward Grefenstette
|ACL 2017
|[http://acl2017.org/tutorials/]
|[https://aclweb.org/anthology/P17-5003/]
|-
|Deep Learning for Dialogue Systems
|Yun-Nung Chen, Asli Celikyilmaz, and Dilek Hakkani-Tur
|ACL 2017
|[http://acl2017.org/tutorials/]
|[https://aclweb.org/anthology/P17-5004/]
|-
|Beyond Words: Deep Learning for Multi-word Expressions and Collocations
|Valia Kordoni
|ACL 2017
|[http://acl2017.org/tutorials/]
|[https://aclweb.org/anthology/P17-5005/]
|-
|Making Better Use of the Crowd
|Jennifer Wortman Vaughan
|ACL 2017
|[http://acl2017.org/tutorials/]
|[https://aclweb.org/anthology/P17-5006/]
|}

==2016 tutorials==

{| border="1" cellspacing="0" cellpadding="5"
|'''Title'''
|'''Trainers'''
|'''Conference'''
|'''Conference link'''
|'''ACL Anthology link'''
|-
|Multimodal Learning and Reasoning
|Desmond Elliott, Douwe Kiela and Angeliki Lazaridou
|ACL 2016
|[http://mirror.aclweb.org/acl2016/index4d04.html?article_id=59]
|[https://www.aclweb.org/anthology/P16-5001/]
|-
|NLP Approaches to Computational Argumentation
|Noam Slonim, Iryna Gurevych, Chris Reed and Benno Stein
|ACL 2016
|[http://mirror.aclweb.org/acl2016/indexfc69.html?article_id=56]
|
|-
|Computer Aided Translation
|Philipp Koehn
|ACL 2016
|[http://mirror.aclweb.org/acl2016/index6ea2.html?article_id=62]
|
|-
|Semantic Representations of Word Senses and Concepts
|José Camacho-Collados, Ignacio Iacobacci, Roberto Navigli and Mohammad Taher Pilehvar
|ACL 2016
|[http://mirror.aclweb.org/acl2016/index6b9c.html?article_id=58]
|
|-
|Neural Machine Translation
|Thang Luong, Kyunghyun Cho and Christopher D. Manning
|ACL 2016
|[http://mirror.aclweb.org/acl2016/index2e51.html?article_id=55]
|
|-
|Game Theory and Natural Language: Origin, Evolution and Processing
|Rocco Tripodi and Marcello Pelillo
|ACL 2016
|[http://mirror.aclweb.org/acl2016/index56cf.html?article_id=57]
|
|-
|Understanding Short Texts
|Zhongyuan Wang and Haixun Wang
|ACL 2016
|[http://mirror.aclweb.org/acl2016/indexedf3.html?article_id=60]
|
|-
|MetaNet: Repository, Identification System, and Applications
|Miriam R L Petruck and Ellen K Dodge
|ACL 2016
|[http://mirror.aclweb.org/acl2016/index6a61.html?article_id=61]
|
|-
|English Resource Semantics
|Dan Flickinger, Emily M. Bender, and Woodley Packard
|NAACL 2016
|[http://naacl.org/naacl-hlt-2016/t1.html]
|[https://aclweb.org/anthology/N16-4001/]
|-
|Multilingual Multimodal Language Processing Using Neural Networks
|Mitesh M Khapra and Sarath Chandar
|NAACL 2016
|[http://naacl.org/naacl-hlt-2016/t2.html]
|[https://aclweb.org/anthology/N16-4002/]
|-
|Question Answering with Knowledge Base, Web and Beyond
|Scott Wen-tau Yih & Hao Ma
|NAACL 2016
|[http://naacl.org/naacl-hlt-2016/t3.html]
|[https://aclweb.org/anthology/N16-4003/]
|-
|Recent Progress in Deep Learning for NLP
|Zhengdong Lu and Hang Li
|NAACL 2016
|[http://naacl.org/naacl-hlt-2016/t4.html]
|[https://aclweb.org/anthology/N16-4004/]
|-
|Scalable Statistical Relational Learning for NLP
|William Yang Wang and William W. Cohen
|NAACL 2016
|[http://naacl.org/naacl-hlt-2016/t5.html]
|[https://aclweb.org/anthology/N16-4005/]
|-
|Statistical Machine Translation between Related Languages
|Pushpak Bhattacharyya, Mitesh Khapra, and Anoop Kunchukuttan
|NAACL 2016
|[http://naacl.org/naacl-hlt-2016/t6.html]
|[https://aclweb.org/anthology/N16-4006/]
|-
|Practical Neural Networks for NLP: From Theory to Code
|Chris Dyer, Yoav Goldberg and Graham Neubig
|EMNLP 2016
|[https://www.aclweb.org/mirror/emnlp2016/tutorials.html#practical]
|
|-
|Advanced Markov Logic Techniques for Scalable Joint Inference in NLP
|Deepak Venugopal, Vibhav Gogate and Vincent Ng
|EMNLP 2016
|[https://www.aclweb.org/mirror/emnlp2016/tutorials.html#advanced]
|
|-
|Lifelong Machine Learning for Natural Language Processing
|Zhiyuan Chen and Bing Liu
|EMNLP 2016
|[https://www.aclweb.org/mirror/emnlp2016/tutorials.html#lifelong]
|
|-
|Neural Networks for Sentiment Analysis
|Yue Zhang and Duy Tin Vo
|EMNLP 2016
|[https://www.aclweb.org/mirror/emnlp2016/tutorials.html#neural]
|
|-
|Continuous Vector Spaces for Cross-language NLP Applications
|Rafael E. Banchs
|EMNLP 2016
|[https://www.aclweb.org/mirror/emnlp2016/tutorials.html#continuous]
|
|-
|Methods and Theories for Large-scale Structured Prediction
|Xu Sun and Yansong Feng
|EMNLP 2016
|[https://www.aclweb.org/mirror/emnlp2016/tutorials.html#methods]
|
|-
|Compositional Distributional Models of Meaning
|Mehrnoosh Sadrzadeh and Dimitri Kartsaklis
|COLING 2016
|[https://coling2016.anlp.jp/tutorials/T1/]
|[https://aclweb.org/anthology/C16-3001/]
|-
|Chinese Textual Sentiment Analysis: Datasets, Resources and Tools
|Lun-Wei Ku and Wei-Fan Chen
|COLING 2016
|[https://coling2016.anlp.jp/tutorials/T2]
|[https://aclweb.org/anthology/C16-3002/]
|-
|Natural Language Processing for Intelligent Access to Scientific Information
|Horacio Saggion and Francesco Ronzano
|COLING 2016
|[https://coling2016.anlp.jp/tutorials/T3]
|[https://aclweb.org/anthology/C16-3003/]
|-
|Quality Estimation for Language Output Applications
|Carolina Scarton, Gustavo Henrique Paetzold, and Lucia Specia
|COLING 2016
|[https://coling2016.anlp.jp/tutorials/T4]
|[https://aclweb.org/anthology/C16-3004/]
|-
|Translationese: Between Human and Machine Translation
|Shuly Wintner
|COLING 2016
|[https://coling2016.anlp.jp/tutorials/T5/]
|[https://aclweb.org/anthology/C16-3005/]
|-
|Succinct Data Structures for NLP-at-Scale
|Matthias Petri and Trevor Cohn
|COLING 2016
|[https://coling2016.anlp.jp/tutorials/T6/]
|[https://aclweb.org/anthology/C16-3006/]
|-
|The Role of Wikipedia in Text Analysis and Retrieval
|Marius Pasca
|COLING 2016
|[https://coling2016.anlp.jp/tutorials/T7]
|[https://aclweb.org/anthology/C16-3007/]
|}

Author: Agata Savary, July 2019

Updates are welcome

2024Q3 Reports: Member at-large / Conference Officer

2024-07-24T11:03:56Z

Yukiar: Submit the Q3 report

* Completed the ACL 2024 Test-of-Time paper award selection. The awardees will be announced at the ACL 2024 award session. The awarded papers and the nomination committee members will be announced at the ACL wiki.

* Finalized the process for selecting the non-anonymized paper awards.

* Updated the ACL conference award policy to accommodate much larger numbers of candidates due to the sheer increase in submissions.

* Communicated with Program Chairs of ACL 2024 and EMNLP 2024 to apply the conference awards policy.

* Served as a member of the ARR board to oversee the organization and the improvement of ARR.

ACL Conference Awards Policy

2024-03-16T10:46:44Z

Yukiar: Add non-publicized paper awards and updates on conference paper awards

This document lays out a standard protocol for awards at ACL conferences (Note that for conferences colocated with a non-ACL event there may need to be some adjustments depending on the policies of the other event. Also, this process was not developed for workshops or journals.). The goal of introducing the policy is to have a consistent approach that suits the scale of our conferences today. The policy is designed to highlight work that is interesting along a variety of axes in ways that encourage discussion. Recognizing valuable work in a consistent way also means the value is clearer (e.g., for hiring and tenure cases).

== Award Types and Criteria ==

=== Best Paper Award ===

We define "Best" as work that is particularly fascinating, controversial, surprising, impressive, and/or potentially field-changing.

* "Best Paper Award" & "Best Non-publicized Paper Award": No more than 0.25% of accepted papers, counting all accepted main conference and findings papers, receive the "Best Paper Award". For smaller conferences with less than 2,000 accepted papers, the maximum number of "Best Paper Award" slots should be kept at 6. Additional "Best Non-publicized Paper Award" winners are selected from the submissions that remained anonymous to the public during the whole process. See [[#Additional Award Slots for Unpublicized Work]] for more details, including the award slots. These papers are presented in a plenary session at the conference. PCs should consider scheduling this plenary session early in the conference, to encourage discussion of the papers during the conference.
* "Outstanding Paper Award" & "Outstanding Non-publicized Paper Award": 1.5–2.5% of accepted papers (counting all accepted main conference and findings papers), selected as part of the same process for the "Outstanding Paper Award". This allows for broader recognition of work that meets the criteria. Additional "Outstanding Non-publicized Paper Award" winners are selected from the submissions that remained anonymous to the public during the whole process. See [[#Additional Award Slots for Unpublicized Work]] for more details.
* "Area Chair's Award": Up to one paper per track. These are selected by the SACs for each track.
* Short and long papers are considered together for these awards.

=== Test of Time Award ===

See the policy at [[Test-of-Time Papers Award]].

=== Special Categories ===

Two special awards will recognise work with a particular focus as described below. Papers that receive an award in a special category are also eligible to receive a general award.
* Social Impact Award - For papers that have the potential for significant positive societal impact.
* Resource Award - For papers that announce, describe, and share a fascinating, valuable, or potentially field-changing new resource (e.g., a dataset or knowledge graph).

Program Committee Chairs may also choose to include awards for other categories. Examples
of additional special categories include:
* Software Award - For papers that describe and share (ideally via source code) a fascinating, valuable, or potentially field-changing new piece of software. Note that this is not the same as the Demonstration Paper Award since there could be papers outside of that track that meet this criterion.
* Demonstration Paper Award - For papers in the Demonstration Track.
* Theme Paper Award - For papers on the conference’s theme.
* Linguistic Insight Award - For papers that make a particularly significant contribution to our understanding of language.
* Low-Resource Paper Award - For papers that contribute to work on languages or domains with limited data.
* Interdisciplinary Research Award - For papers that contribute to NLP and another field in new and interesting ways.
* Reproduction Award - For papers that reproduce prior work in a particularly enlightening way, revealing additional features of the prior work.

=== Additional Award Slots for Unpublicized Work ===

In January 2024, the ACL Executive Committee voted to adopt [[Media:ACL_Anonymity_Policy.pdf|recommendations]] including the following:

* Add new best and outstanding paper awards for submissions that remained anonymous to the public during the whole process, to ameliorate the "publicity deficit" for such submissions. (The precise eligibility criteria for these awards would be at the discretion of the awards committee. Of course, such submissions would remain eligible also for all other awards.)
* Non-publicized submissions are eligible for both "Best/Outstanding Paper Award" and "Best/Outstanding Non-publicized Paper Award". There will be one award per paper, and if a paper happens to win both publicized and non-publicized awards, it will remain with the general (non-publicized) award, and an additional non-publicized paper will be sought for the non-publicized award slot.
* All papers are eligible for the regular awards, and only non-publicized papers are eligible for the non-publicized awards. The numbers of non-publicized award slots are calculated based on the number of accepted papers that remained anonymous (e.g., 1.5-2.5% of accepted papers that remained anonymous will receive Outstanding Non-publicized Paper awards). The method of identifying the total number of papers that remained anonymous will be determined by the PCs.

== Selection Process ==

=== Nominations ===

In the review process, reviewers (Note: PCs may choose whether or not to include reviewers in the nomination process.), AEs, ACs, and SACs will be asked to answer the following questions:

* 1. "Could the camera-ready version of this paper merit consideration for an "outstanding paper" award (up to 2.5% of accepted papers will be recognized in this way)? Outstanding papers should be either fascinating, controversial, surprising, impressive, or potentially field-changing. Awards will be decided based on the camera-ready version of the paper."
** "Yes"
** "Maybe"
** "No"
* 2. "If yes/maybe, please briefly describe why:"
** (short answer box with no word or character limit)

The best paper committee should consider any paper that was marked "Yes" or "Maybe" by any reviewer (Note: PCs may choose whether or not consider papers that were labeled 'maybe' by a reviewer and not nominated by the AE/AC.), AE, AC, or SAC (Note: This requirement is included to clarify how this policy interacts with ACL Rolling Review.).

SACs may also choose a paper in their area to receive the "Area Chair's Award". This will encourage diversity in the papers that are highlighted. SACs may not give this award to a paper that one of the SACs in the track has a conflict-of-interest (COI) on, but any other paper in their track can be chosen. Papers that SACs have COIs on can be nominated for other awards. If a conference has a COI track, that track does not select an Area Chair's award. The process for SACs to choose the award is up to the SACs (e.g., whether and how to involve ACs). The Best Paper Committee will not be told which papers have received this award, to avoid biasing their choices.

Papers are not eligible for awards if their authors include Program Chairs. The PCs may also choose to specify that authors in other senior organisational roles are not eligible for awards.

Special category awards can either be handled in the same way, by editing the question above to include them, or through a separate process defined by the PCs.

The eligibility for non-publicized paper awards should be confirmed before notification, i.e., in-between the final acceptance decision by PCs and notification. The best paper committee chairs or program chairs search for paper titles and authors of the candidates at preprint servers, popular social media, or on the web in general.

=== Selection ===

(Note, this process was developed based on the assumption that the nomination process leads to 80-100 papers being considered for awards. If the number of nominated papers turns out to be much higher or lower then the selection process should be adapted as necessary.)

Program chairs should select and appoint a Best Paper Committee Chair(s) early in the process (it is recommended well before the submission deadline), who will manage the entire process. A best paper committee will be selected by the program chairs together with the Best Paper Committee Chair(s). The committee size should be large enough to keep the load to around 10-15 papers per member. The committee should be diverse in composition in terms of research areas and demographics. It is best to form the committee before the review process commences, otherwise it may be hard to recruit members.

The committee will follow a multistage process to determine the final awards. First, the program chairs and committee chair(s) collect anonymized final camera-ready versions of the candidates. If the papers are not anonymized then the committee chair is encouraged to manually redact author information on the first page of the paper. The committee will receive the (anonymized) camera-ready version of the paper, anonymized reviews, and associated supplementary materials. If the papers are not anonymized then the committee chair is encouraged to redact author information on the first page of the paper. Also, the chair should encourage the committee to not look for revealing author information elsewhere (e.g. acceptance list on the conference page).
# Papers are divided between the committee members based on research areas for a first pass in which each paper is read by at least 2 committee members. They independently place the papers into three groups: (1) consider for best paper, (2) consider for outstanding paper, (3) do not consider further.
# All committee members read the papers that both readers placed under consideration for 'best paper'.
# The committee meets to make the final selections.

This proposal does not define a specific rubric. However, while reading and discussing papers, committee members should consider:

* Is each paper either fascinating, controversial, surprising, impressive, or potentially field-changing? Note that papers do not need to demonstrate all of these properties; any property is sufficient.
* Does the paper present as its motivating use case an application with significant negative social impact? Even if the motivating use case is an application with neutral or positive social impact, are there obvious applications with significant negative social impact which are left unaddressed or insufficiently addressed by the paper? (In such cases, the paper should *not* be an award candidate.)
* Is the work presented in the paper reproducible? For example, is there sufficient information in the paper to repeat the experiments? If not, is the lack of reproducibility justified in the paper?
* Do the awards highlight a broad range of research types and strengths?
* Do the awards include types of research that can be conducted at small labs?
* Do the awards include papers that show excellence in potential positive social impact?

Special awards may be selected by the best paper committee or by separate committees. All award decisions may be overruled by the PCs.

== Announcement, Certificates, and Financial Awards ==

Announcement: All awardees will be announced on the conference website or blog before the conference. Titles and authors do not need to be announced during a plenary session.

Certificates: All awardees will receive a certificate with the name of the conference, name of the award, title of the paper, and author names, signed by the Program Chair(s) and/or General Chair(s).

Financial Awards: Conferences may choose to have a financial component for any of the awards, which may be sponsored, and may be split between the authors as the authors choose.

== Documentation ==

The ACL Anthology will have an indication of which papers received awards (as it does now). Eventually, a page will be created that lists all papers that have received awards.

The ACL will track the research area and author demographics (gender and country of affiliation) of papers that are (a) nominated, and (b) receive awards. This information will be used to inform efforts to avoid bias and inequality in the selection process. Care should be taken to track them appropriately to avoid harm (e.g., by incorrectly assuming a demographic attribute). The ACL Equity Director will be responsible for maintaining these records.

== Notes on the development of this proposal ==

Committee that developed this policy:
* Bonnie Webber
* Christy Doran
* Huang Xuanjing
* Joel Tetreault
* Jonathan Kummerfeld [chair]
* Yusuke Miyao [chair]

Edits were made by Jonathan Kummerfeld based on discussion with the ACL Executive. Feedback was solicited from the community and informed further improvements.

=== Anonymity ===

We discussed requiring authors to be anonymous to avoid potential a source of bias, but realised it was (1) not easy to do with camera-ready versions of papers, and (2) would be difficult to maintain while also handling conflict-of-interests on the best paper committee (COIs will be more likely than in the past because more papers are being considered). Given those concerns, we have made it optional.

=== Choice of terms ===

The committee had mixed opinions on the term 'best'. On one hand, it is common/standard and expected, on the other hand, it implies a metric with a clear ranking of papers, which we do not have. After discussing a range of alternatives we returned to 'best' because it is such a well established term. Several options for the second category were discussed, including 'noteworthy', 'honourable mention', and 'outstanding'. The final proposal uses 'outstanding' as it
is a recognisable term.

=== Choice of numbers ===

* "Typically no more than 6" best papers. This value was chosen based on what we thought could be included in a single plenary session. However, the language is intentionally flexible.
* "1.5–2.5% of accepted papers" receive the outstanding paper award. This range was chosen to balance prestige (which favors a lower number) and the goal to recognize more work (which favors a higher number).
* For context, the graph below shows historical trends in awards at ACL going back to the very first year a best paper award was announced (note, these values include all award types together in the count for each year):

[[File: papers receiving awards at ACL.png|800px]]

=== Several ideas came up that are worth considering in the future: ===

* Dissertation Awards. These exist in other communities (e.g. AAAI) and are intended to recognise a body of work. We did not include them as they are outside of the conference awards process. They would need a separate process that could, for example, be conducted by each organisation / society separately. We also discussed awards for thesis proposals, which would be suitable for the Student Research Workshop.
* Journal Paper Awards. Other communities have these (e.g. ISCA for papers in CSL). We discussed including them here by saying CL and TACL papers presented at a conference are eligible for awards, but then the awards committee needs to compare quite different papers (e.g. a 30+ page journal article and a 4 page short conference paper). The approach used elsewhere is that each journal chooses a 'best paper', but that is then outside the scope of this proposal regarding conferences.
* Requirements for papers to be well-presented. We discussed the idea that papers that receive awards should be examples of well-presented ideas that students can look to for understanding how to write a good paper. On the other hand, it would seem unfair to not reward an innovative idea just because the author is inexperienced and did not present it perfectly. We tried to come up with language to balance these factors, but decided there was no simple solution, so this requirement was dropped.
* Advice/guidance for reviewers on when to select a paper for an award. This is a good idea that is outside the scope of this policy.

2024Q1 Reports: Member at-large - Asia/Pacific

2024-02-15T07:55:47Z

Yukiar: Created page with "As the conference officer, I worked on the following issues regarding conference organisation. * Started the process of the ACL 2024 Test-of-Time paper award nomination ** T..."

As the conference officer, I worked on the following issues regarding conference organisation.

* Started the process of the ACL 2024 Test-of-Time paper award nomination
** The co-chairs are determined and are in the process of selecting nomination committee members.
* Started the implementation of non-publicized paper awards