The second annual West Coast NLP (WeCNLP) Summit is an opportunity to foster discussion and collaboration between NLP researchers in academia and industry. The event will include talks and a panel from research leaders on the latest advances in NLP technologies. The day will conclude with a poster session where attendees can learn from each other in an informal setting. The event will take place at Facebook, Menlo Park, on September 6th.


  • June 7 2019 Abstract submissions are open! The deadline has been extended to July 11 (UTC-12). If you are a student, please check below the travel grant section as students with an accepted abstract will be eligible for a travel grant.
  • April 30 2019 Registration is open!

Important Dates

  • June 30, 2019 – Poster abstract submission deadline
  • July 22 2019 – Notification of abstract acceptance
  • September 6 2019 – WeCNLP Summit

Call for Abstracts

We invite submissions in areas of NLP related, but not limited to, the following topics:

  • bias and ethics in NLP.
  • multimodal NLP.
  • dialog and conversational AI.
  • low resource scenarios in NLP.

Submissions should describe new, previously, or concurrently published research. We welcome abstract submissions, in theory, methodology, as well as applications. Abstracts may describe completed research or work-in-progress. We also welcome abstract submissions on negative results as well as challenges faced in industrial or academic applications.

Accepted abstracts will be presented at the poster session. Selected abstracts will be presented both as a lightning talk and at the poster session.

Multiple submissions and anonymity policies: WeCNLP 2019 is compatible with EMNLP and CoNLL multiple submissions and anonymity policies - WeCNLP 2019 is non archival and accepted abstracts concurrently submitted to EMNLP or CoNLL will be publicly announced only after the corresponding anonymity period has ended.

Style Guidelines

  • Abstracts must not include identifying information
  • Abstracts must be no more than 1 page, excluding references. The main body text must be 11 points in size.
  • Do not include any supplementary files with your submission.

Content Guidelines

  • Your abstract should stand alone, without linking to a longer paper or supplement.
  • You should convey motivation and give some technical details of the approach used.
  • While we appreciate that space is limited, some experimental results are likely to improve reviewers' opinions of your abstract.

Acceptance Criteria

Abstracts will be reviewed by at least two reviewers, who will use the following criteria:

  • Is this abstract appropriate for WeCNLP? I.e. Does it describe research in NLP?
  • Does the abstract describe work that is novel and/or an interesting application?
  • Does the abstract adequately convey the material that will be presented?

Steering Committee

Jeff Dean

Emily Fox

Zoubin Ghahramani

Dilek Hakkani-Tur

Yann LeCun

Chris Manning

Invited Speakers

Zhou Yu

Zhou is an Assistant Professor at the Computer Science Department in UC Davis. She received her PhD in Carnegie Mellon University. Zhou's research centers on dialog systems and language generation. She was recently featured in Forbes as 2018 30 under 30 in Science. Her team won Amazon Alexa Prize 2018 with $500,000 award in building a social chatbot. ( She was also a recipient of Rising stars in EECS in 2015. Zhou's website.

Marc'Aurelio Ranzato

Marc'Aurelio Ranzato is a Research Scientist at Facebook AI Research lab in New York City. His research interests are in the area of unsupervised learning, continual learning and transfer learning, with applications to vision, natural language processing and speech recognition. Marc'Aurelio has earned a PhD in Computer Science at New York University under Yann LeCun's supervision. After a postdoc with Geoffrey Hinton at University of Toronto, he joined the Google Brain team in 2011. In 2013 he joined Facebook and was a founding member of the Facebook AI Research lab.

Heng Ji

Heng Ji is Professor of Computer Science at University of Illinois at Urbana-Champaign. She received her Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Information Extraction (IE) and Knowledge Base Population. She is selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she has received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013 and NSF CAREER award in 2009. She is the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing. She served as the Program Committee Chair of many conferences including NAACL2018.

Louis-Philippe Morency

Louis-Philippe Morency is Leonardo Associate Professor in the Language Technology Institute at Carnegie Mellon University where he leads the Multimodal Communication and Machine Learning Laboratory (MultiComp Lab). He was formerly research faculty in the Computer Sciences Department at University of Southern California and received his Ph.D. degree from MIT Computer Science and Artificial Intelligence Laboratory. His research focuses on building the computational foundations to enable computers with the abilities to analyze, recognize and predict subtle human communicative behaviors during social interactions. He received diverse awards including AI’s 10 to Watch by IEEE Intelligent Systems, NetExplo Award in partnership with UNESCO and 10 best paper awards at IEEE and ACM conferences. His research was covered by media outlets such as Wall Street Journal, The Economist and NPR. He is currently chair of the advisory committee for ACM International Conference on Multimodal Interaction and associate editor at IEEE Transactions on Affective Computing.

Aylin Caliskan

Aylin Caliskan is an assistant professor of computer science at George Washington University. Her research interests include the emerging science of bias in artificial intelligence, fairness in machine learning, and privacy. Her work aims to characterize and quantify aspects of natural and artificial intelligence using a multitude of machine learning, language processing, and computer vision techniques. In her recent publication in Science, she demonstrated how semantics derived from language corpora contain human-like biases. Prior to that, she developed novel privacy attacks to de-anonymize programmers using code stylometry. Her presentations on both de-anonymization and bias in machine learning are the recipients of best talk awards. Her work on semi-automated anonymization of writing style furthermore received the Privacy Enhancing Technologies Symposium Best Paper Award. Her research has received extensive press coverage across the globe. Aylin holds a PhD in Computer Science from Drexel University and a Master of Science in Robotics from University of Pennsylvania. Before joining the faculty at George Washington University, she was a postdoctoral researcher and a fellow at Princeton University's Center for Information Technology Policy.

Jason Williams

Jason Williams manages the language understanding group for Siri, at Apple. Before joining Apple, he was a Research Manager at Microsoft Research, where he led the Conversational Systems Research Group and the Redmond Reinforcement Learning Group. Prior to Microsoft, he was Principal Researcher with AT&T Labs – Research. He has published about 60 peer-reviewed papers on dialog systems and related areas, and has received five best paper/presentation awards for work on statistical approaches to dialog systems, including the use of POMDPs (partially observable Markov decision processes), reinforcement learning, turn-taking, and empirical user studies. In 2012 he initiated the Dialog State Tracking Challenge series, in 2014 he shipped components of the first release of Microsoft Cortana, in 2015 he launched Microsoft’s Language Understanding Service, and in 2018 he launched Microsoft’s Conversation Learner Service. He is President of SIGDIAL, and an elected member of the IEEE Speech and Language Technical Committee (SLTC) in the area of spoken dialogue systems.

Jingjing Liu

Jingjing Liu is a Principal Research Manager at Microsoft, leading a research group in Conversational AI. Her current research interests center on Vision + Language Multimodal Intelligence, such as Visual QA/Dialog, Visual Reasoning, Dialog-based Image Synthesis, and Story Visualization. Dr. Liu received her PhD degree in Computer Science from MIT EECS in 2011, with a research focus on Spoken Dialog Systems. She also holds an MBA degree from Judge Business School at University of Cambridge. Before joining MSR, Dr. Liu was a Research Scientist at MIT CSAIL and Director of Product at the unicorn startup Mobvoi Inc.

Emily Bender

Emily M. Bender is a Professor in the Department of Linguistics and Adjunct Professor in the School of Computer Science & Engineering at the University of Washington. She is also affiliated with UW's Tech Policy Lab and Value Sensitive Design Lab. Her primary research interests lie in multilingual grammar engineering, the incorporation of linguistic knowledge in NLP, computational semantics, and ethics in NLP. She is the author of Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax (2013, Morgan & Claypool) and, with Alex Lascarides, the forthcoming follow-up volume Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Semantics and Pragmatics. She completed her PhD in Linguistics at Stanford University in 2000.

Mari Ostendorf

Mari joined the University of Washington Electrical & Computer Engineering Department in 1999. She is an Endowed Professor of System Design Methodologies in the ECE Department, an Adjunct Professor in CSE and Linguistics, as well as Associate Vice Provost for Research. She is a Fellow of the IEEE, ISCA and ACL, a Scottish Informatics and Computer Science Alliance Distinguished Visiting Fellow, and a former Australian-American Fulbright Scholar. Prof. Ostendorf has published over 270 papers on a variety of topics in speech and language processing. In 2017, she served as a faculty advisor for the student team winning the inaugural Alexa Prize competition to build a socialbot, and conversational AI is a focus of her current work. Her research explores dynamic models for understanding and generating speech and text, particularly in multi-party contexts, and it contributes to a variety of applications, from education to clinical and scientific information extraction.

Greg Durrett

Greg Durrett is an assistant professor of Computer Science at UT Austin. His current research focuses on a range of topics in statistical natural language processing, including information extraction (particularly coreference resolution and entity linking), document summarization, and question answering. He is particularly interested in endowing deep models with the ability to do discrete reasoning and logical inference. Greg completed his Ph.D. at UC Berkeley in 2016, and from 2016-2017, he was a research scientist at Semantic Machines.

Dr. Gokhan Tur

Dr. Gokhan Tur is a leading artificial intelligence expert on human/machine conversational language understanding systems. He co-authored more than 150 papers published in journals or books and presented at conferences. He is the editor of the book entitled "Spoken Language Understanding" by Wiley in 2011. He received the Ph.D. degree in Computer Science from Bilkent University, Turkey in 2000. Between 1997 and 1999, he was a visiting scholar at the CMU LTI, then the Johns Hopkins University, and the Speech Lab of SRI, CA. At AT&T Research, NJ (2001-2006) he worked on pioneering conversational systems like "How May I Help You?". He worked for the DARPA GALE and CALO projects at the Speech Lab of SRI, CA (2006-2010). He was a founding member of the Microsoft Cortana team, and later the Conversational Systems Lab at Microsoft Research (2010-2016). He worked as the Conversational Understanding Architect at Apple Siri team (2014-2015) and as the Deep Conversational Understanding Technical Lead Manager at Google Research. He is currently with the Uber AI Labs.

Travel Grant

We are setting up a travel grant to encourage more participation from students that are not located in the Bay Area. Students currently enrolled at an accredited university are eligible. Travel grants will be available to students whose abstracts are accepted. The application link will be available soon.


Coming soon.

Organizing Committee

  • Dan Iter (Stanford)
  • Juan Ignacio Cases Martin (Stanford)
  • Sharon Zhou (Stanford)
  • Andrew Maas (Stanford)
  • Anna Goldie (Google)
  • Andrew Dai (Google)
  • Mona Diab (Amazon)
  • Ankur Gandhe (Amazon)
  • Udhyakumar Nallasamy (Apple)
  • Ashish Garg (Apple)
  • Sachin Agarwal (Apple)
  • Kyle Williams (Microsoft)
  • Alex Marin (Microsoft)
  • Nanyun Peng (University of Southern California)
  • Xiang Ren (University of Southern California)
  • Antoine Bosselut (University of Washington)
  • Yi-Chia Wang (Uber)
  • Chandra Khatri (Uber)
  • Luke Zettlemoyer (Facebook)
  • Alborz Geramifard (Facebook)
  • Myle Ott (Facebook)
  • Chris Moghbel (Facebook)
  • Necip Fazil Ayan (Facebook)
  • Juan Pino (Facebook)