This cycle (2020-2021) I will be on the job market!

My email address is: jkummerf@umich.edu

My CV is available as a pdf.

I am a Postdoctoral Research Fellow, working on Natural Language Processing at the University of Michigan, in Computer Science and Engineering. While at Michigan I have worked on a range of sub-areas including Crowdsourcing, Code Generation and Dialogue.

I completed my PhD in the UC Berkeley NLP Group, advised by Dan Klein, with a thesis on new algorithms related to syntactic parsing: error analysis, formalism conversion, and graph parsing. I completed my BSc (Adv) with honours and medal in the University of Sydney Schwa Lab advised by James Curran, with a thesis on an algorithm for faster CCG parsing. I received my Higher School Certificate at Emanuel School, receiving the Premier’s Award for my results in English, Mathematics, Physics, and Cosmology.

Interests

  • Computational Linguistics / Natural Language Processing
  • Artificial Intelligence
  • Crowdsourcing

Education

  • PhD in Computer Science, 2016

    University of California, Berkeley

  • BSc (Adv) (Hons I) and Medal in Computer Science, 2009

    University of Sydney

  • Higher School Certificate, 2005

    Emanuel School

Publications


Preprints

Laura Burdick, Jonathan K. Kummerfeld, Rada Mihalcea
ArXiv, 2020

2020

Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, Rada Mihalcea
EMNLP, 2020
Charles Welch, Rada Mihalcea, Jonathan K. Kummerfeld
EMNLP (short), 2020
Stefan Larson, Anthony Zheng, Anish Mahendran, Rishi Tekriwal, Adrian Cheung, Eric Guldan, Kevin Leach, Jonathan K. Kummerfeld
EMNLP (short), 2020
Youxuan Jiang, Huaiyu Zhu, Jonathan K. Kummerfeld, Yunyao Li, Walter Lasecki
Findings of EMNLP, 2020
Jordan S. Huffaker, Jonathan K. Kummerfeld, Walter S. Lasecki, Mark S. Ackerman
CHI, 2020
Luis Fernando D'Haro, Koichiro Yoshino, Chiori Hori, Tim K. Marks, Lazaros Polymenakos, Jonathan K. Kummerfeld, Michel Galley, Xiang Gao
CSL, 2020

2019

Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagné, Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron Courville
NeurIPS, 2019
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, Jason Mars
EMNLP (short), 2019
Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph J. Peper, Vignesh Athreya, Chulaka Gunasekara, Jatin Ganhotra, Siva Sankalp Patel, Lazaros Polymenakos, Walter S. Lasecki
ACL, 2019
Jonathan K. Kummerfeld
ACL (demo), 2019
Stefan Larson, Anish Mahendran, Andrew Lee, Jonathan K. Kummerfeld, Parker Hill, Michael Laurenzano, Johann Hauswald, Lingjia Tang, Jason Mars
NAACL, 2019
Charles Welch, Verónica Pérez-Rosas, Jonathan K. Kummerfeld, Rada Mihalcea
Best Student Paper - CICLing, 2019
Charles Welch, Verónica Pérez-Rosas, Jonathan K. Kummerfeld, Rada Mihalcea
IEEE Intelligent Systems, 2019

2018

Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, Dragomir Radev
ACL, 2018
Yiping Kang, Yunqi Zhang, Jonathan K. Kummerfeld, Parker Hill, Johann Hauswald, Michael A. Laurenzano, Lingjia Tang, Jason Mars
NAACL (industry), 2018
Laura Burdick, Jonathan K. Kummerfeld, Rada Mihalcea
NAACL, 2018
Youxuan Jiang, Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Walter Lasecki
NAACL (short), 2018
Charles Welch, Jonathan K. Kummerfeld, Song Feng, Rada Mihalcea
LREC, 2018

2017

Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca S. Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, Vern Paxson
EMNLP, 2017
Youxuan Jiang, Jonathan K. Kummerfeld, Walter S. Lasecki
ACL (short), 2017
Rebecca S. Portnoff, Sadia Afroz, Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Damon McCoy, Kirill Levchenko, Vern Paxson
WWW, 2017
Jonathan K. Kummerfeld, Dan Klein
TACL, 2017

2016

Jonathan K. Kummerfeld
EECS Department, University of California, Berkeley, 2016

2015

Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Dan Klein
EMNLP (short), 2015

2013

Jonathan K. Kummerfeld, Dan Klein
EMNLP, 2013
Jonathan K. Kummerfeld, Daniel Tse, James R. Curran, Dan Klein
ACL (short), 2013
Vanessa A. Moss, Naomi M. McClure-Griffiths, Tara Murphy, D. J. Pisano, Jonathan K. Kummerfeld, James R. Curran
The Astrophysical Journal Supplement Series, 2013

2012

Jonathan K. Kummerfeld, David Hall, James R. Curran, Dan Klein
EMNLP, 2012
Jonathan K. Kummerfeld, Dan Klein, James R. Curran
ACL (short), 2012

2011

Jonathan K. Kummerfeld, Mohit Bansal, David Burkett, Dan Klein
CoNLL Shared Task, 2011

2010

Raphael Candelier, Asaph Widmer-Cooper, Jonathan K. Kummerfeld, Olivier Dauchot, Giulio Biroli, Peter Harrowell, David R. Reichman
Physical Review Letters, 2010
Matthew Honnibal, Jonathan K. Kummerfeld, James R. Curran
CoLing, 2010
Jonathan K. Kummerfeld, Jessika Roesner, Tim Dawborn, James Haggerty, James R. Curran, Stephen Clark
ACL, 2010

2009

Jonathan K. Kummerfeld, Jessika Roesner, James R. Curran
ALTA, 2009
Jonathan K. Kummerfeld
The University of Sydney, 2009

2008

Jonathan K. Kummerfeld, James R. Curran
ALTA, 2008
Jonathan K. Kummerfeld, Toby S Hudson, Peter Harrowell
The Journal of Physical Chemistry B, 2008

Non-Archival

2020

Jonathan K. Kummerfeld
HComp (Work in Progress), 2020
Chulaka Gunasekara, Jonathan K. Kummerfeld, Luis Lastras, Walter S. Lasecki
AAAI Wokshop: Dialogue System Technology Challenges, 2020

2019

Seokhwan Kim, Michel Galley, Chulaka Gunasekara, Sungjin Lee, Adam Atkinson, Baolin Peng, Hannes Schulz, Jianfeng Gao, Jinchao Li, Mahmoud Adada, Minlie Huang, Luis Lastras, Jonathan K. Kummerfeld, Walter S. Lasecki, Chiori Hori, Anoop Cherian, Tim K. Marks, Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta
NeurIPS Workshop: Conversational AI: Today's Practice and Tomorrow's Potential, 2019
Jordan S. Huffaker, Jonathan K. Kummerfeld, Walter S. Lasecki, Mark S. Ackerman
CSCW Workshop: Volunteer Work: Mapping the Future of Moderation Research, 2019
Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, Walter S. Lasecki
ACL Workshop: NLP for Conversational AI, 2019
Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, Walter S. Lasecki
AAAI Wokshop: Dialogue System Technology Challenges, 2019

2018

Koichiro Yoshino, Chiori Hori, Julien Perez, Luis Fernando D'Haro, Lazaros Polymenakos, Chulaka Gunasekara, Walter S. Lasecki, Jonathan K. Kummerfeld, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan, Xiang Gao, Huda Alamari, Tim K. Marks, Devi Parikh, Dhruv Batra
NeurIPS Workshop: Conversational AI: Today's Practice and Tomorrow's Potential, 2018

2009

Stephen Clark, Ann Copestake, James R. Curran, Yue Zhang, Aurelie Herbelot, James Haggerty, Byung-Gyu Ahn, Curt Van Wyk, Jessika Roesner, Jonathan K. Kummerfeld, Tim Dawborn
Johns Hopkins University, 2009

Recent Posts

Papers I’m reading and more (RSS Feed and E-mail List)

A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels (Jiang, et al., Findings of EMNLP 2020)

My previous post discussed work on crowdsourcing QA-SRL, a way of capturing semantic roles in text by asking workers to answer questions. This post covers a paper I contributed to that also considers crowdsourcing SRL, but collects the more traditional form of annotation used in resources like Propbank.

Controlled Crowdsourcing for High-Quality QA-SRL Annotation (Roit, et al., ACL 2020)

Semantic Role Labeling captures the content of a sentence by labeling the word sense of the verbs and identifying their arguments. Over the last few years, Luke Zettlemoyer’s Group has been exploring using question-answer pairs to represent this structure. This approach has the big advantage that it is easier to explain than the sense inventory and role types of more traditional SRL resources like PropBank. However, even with that advantage, crowdsourcing this annotation is difficult, as this paper shows.

Software

Colaboratoy Notebook for Coreference Resolution with SpanBERT

A notebook that (1) sets up the SpanBERT code and model, and (2) runs inference on text you provide.

SLATE: A Super-Lightweight Annotation Tool for Experts

A terminal-based text annotation tool in Python.

Neural POS tagging

Implementations of a POS tagger in DyNet, PyTorch, and Tensorflow, visualised to show the overall picture and make comparisons easy.

Text to SQL Baseline

A simple LSTM-based model that uses templates and slot-filing to map questions to SQL queries.

One-Endpoint Crossing Graph Parser

A range of tools related to one-endpoint crossing graphs - parsing, format conversion, and evaluation.

Coreference Error Analysis

A tool for classifying errors in coreference resolution.

CCG to PST

A tool for converting CCG derivations into PTB-style phrase structure trees.

Parse Error Analysis

A tool for classifying mistakes in the output of parsers.

Data

DSTC 7 track 1: Next Utterance Selection

Data from Noetic End-to-End Response Selection Challenge. Dialogue from Ubuntu tech support and Michigan course advising.

DSTC 8 track 2: Next Utterance Selection

Data from NOESIS II: Predicting Responses, Identifying Success, and Managing Complexity in Task-Oriented Dialogue. Dialogue from Ubuntu tech support and Michigan course advising.

IRC Disentanglement

Annotation of IRC messages with reply-to structure, which disentangles simultaneous conversations. The largest such annotated resource.

Text to SQL datasets

A collection of datasets containing questions in English paired with SQL queries for a provided database. Our version homogenises the style of the SQL and corrects errors in previous versions of the data.

IE/NER from Cybercriminal Forums

Forum posts with annotations of products.

Crowdsourced Paraphrases

Paraphrases collected while conducting experiments on factors influencing crowd performance.

Spine and Arc version of the Penn Treebank

Code to convert the standard Penn Treebank into a version where each word is assigned a spine of non-terminals, and arcs to indicate attachments from one spine to another.

Adaptive CCG Supertagging Model

A model for the C&C supertagger that gives the same results with smaller beam sizes, enabling faster parsing.

Contact