Search Results for author: Dan Roth

Found 215 papers, 58 papers with code

There’s a Time and Place for Reasoning Beyond the Image

1 code implementation ACL 2022 Xingyu Fu, Ben Zhou, Ishaan Chandratreya, Carl Vondrick, Dan Roth

Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture.

Image Clustering

Quantifying Clinical Outcome Measures in Patients with Epilepsy Using the Electronic Health Record

no code implementations BioNLP (ACL) 2022 Kevin Xie, Brian Litt, Dan Roth, Colin A. Ellis

A wealth of important clinical information lies untouched in the Electronic Health Record, often in the form of unstructured textual documents.

Text Summarization

Building Low-Resource NER Models Using Non-Speaker Annotations

no code implementations NAACL (DaSH) 2021 Tatiana Tsygankova, Francesca Marini, Stephen Mayhew, Dan Roth

In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.

Low Resource Named Entity Recognition named-entity-recognition +2

ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations

no code implementations EMNLP 2021 Rujun Han, I-Hung Hsu, Jiao Sun, Julia Baylon, Qiang Ning, Dan Roth, Nanyun Peng

While these tasks partially evaluate machines’ ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.

Machine Reading Comprehension

Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0

no code implementations Findings (EMNLP) 2021 Elior Sulem, Jamaal Hay, Dan Roth

Understanding when a text snippet does not provide a sought after information is an essential part of natural language utnderstanding.

On the Effects of Transformer Size on In- and Out-of-Domain Calibration

no code implementations Findings (EMNLP) 2021 Soham Dan, Dan Roth

To reduce the cost of training such large models, prior work has developed smaller, more compact models which achieves a significant speedup in training time while maintaining competitive accuracy to the original model on downstream tasks.

Natural Language Processing

Compositional Data and Task Augmentation for Instruction Following

no code implementations Findings (EMNLP) 2021 Soham Dan, Xinran Han, Dan Roth

Executing natural language instructions in a physically grounded domain requires a model that understands both spatial concepts such as “left of” and “above”, and the compositional language used to identify landmarks and articulate instructions relative to them.

Few-Shot Novel Concept Learning for Semantic Parsing

no code implementations Findings (EMNLP) 2021 Soham Dan, Osbert Bastani, Dan Roth

This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept.

Program Synthesis Semantic Parsing

Understanding the Extent to which Content Quality Metrics Measure the Information Quality of Summaries

no code implementations CoNLL (EMNLP) 2021 Daniel Deutsch, Dan Roth

Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference.

Question Answering

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

1 code implementation9 Jun 2022 Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, César Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodola, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Şenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramírez Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramón Risco Delgado, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima, Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Timothy Telleen-Lawton, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, ZiRui Wang, Ziyi Wu

BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.

Common Sense Reasoning

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner

no code implementations18 May 2022 Danilo Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henry Zhu, Xinchi Chen, Zhiheng Huang, Peng Xu, Andrew Arnold, Dan Roth

Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.

Question Answering

Repro: An Open-Source Library for Improving the Reproducibility and Usability of Publicly Available Research Code

1 code implementation29 Apr 2022 Daniel Deutsch, Dan Roth

We introduce Repro, an open-source library which aims at improving the reproducibility and usability of research code.

Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics

no code implementations Findings (ACL) 2022 Daniel Deutsch, Dan Roth

Question answering-based summarization evaluation metrics must automatically determine whether the QA model's prediction is correct or not, a task known as answer verification.

Question Answering

Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics

no code implementations21 Apr 2022 Daniel Deutsch, Rotem Dror, Dan Roth

How reliably an automatic summarization evaluation metric replicates human judgments of summary quality is quantified by system-level correlations.

DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization

no code implementations ACL 2022 Zheng Li, Zijian Wang, Ming Tan, Ramesh Nallapati, Parminder Bhatia, Andrew Arnold, Bing Xiang, Dan Roth

Empirical analyses show that, despite the challenging nature of generative tasks, we were able to achieve a 16. 5x model footprint compression ratio with little performance drop relative to the full-precision counterparts on multiple summarization and QA datasets.

Quantization Text Generation

There is a Time and Place for Reasoning Beyond the Image

1 code implementation1 Mar 2022 Xingyu Fu, Ben Zhou, Ishaan Preetam Chandratreya, Carl Vondrick, Dan Roth

For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understandings of the signs, the buildings, the crowds, and more.

Image Clustering

Understanding Robust Generalization in Learning Regular Languages

no code implementations20 Feb 2022 Soham Dan, Osbert Bastani, Dan Roth

Currently, deep neural networks struggle to generalize robustly to such shifts in the data distribution.

ROCK: Causal Inference Principles for Reasoning about Commonsense Causality

1 code implementation31 Jan 2022 Jiayao Zhang, Hongming Zhang, Weijie J. Su, Dan Roth

Commonsense causality reasoning (CCR) aims at identifying plausible causes and effects in natural language descriptions that are deemed reasonable by an average person.

Causal Inference

Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval

1 code implementation28 Jan 2022 Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig

Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time.

Language Modelling

Design Challenges for a Multi-Perspective Search Engine

1 code implementation15 Dec 2021 Sihao Chen, Siyi Liu, Xander Uyttendaele, Yi Zhang, William Bruno, Dan Roth

Naturally, identifying such responses within a document is a natural language understanding task.

Natural Language Understanding

Event Linking: Grounding Event Mentions to Wikipedia

no code implementations15 Dec 2021 Xiaodong Yu, Wenpeng Yin, Nitish Gupta, Dan Roth

Event linking tries to link an event mention, appearing in a news article for example, to the most appropriate Wikipedia page.

Natural Language Understanding

Question-Based Salient Span Selection for More Controllable Text Summarization

no code implementations15 Nov 2021 Daniel Deutsch, Dan Roth

In this work, we propose a method for incorporating question-answering (QA) signals into a summarization model.

Question Answering Text Summarization

Learning Constraints and Descriptive Segmentation for Subevent Detection

no code implementations EMNLP 2021 Haoyu Wang, Hongming Zhang, Muhao Chen, Dan Roth

The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes.

Text Segmentation

What is Your Article Based On? Inferring Fine-grained Provenance

no code implementations ACL 2021 Yi Zhang, Zachary Ives, Dan Roth

We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from \url{www. politifact. com}; our experimental results show that our solution leads to a significant improvement over baselines.

Fact Checking

Zero-shot Event Extraction via Transfer Learning: Challenges and Insights

no code implementations ACL 2021 Qing Lyu, Hongming Zhang, Elior Sulem, Dan Roth

Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies.

Event Extraction Natural Language Inference +2

Event-Centric Natural Language Processing

no code implementations ACL 2021 Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth

This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.

Natural Language Processing

MultiOpEd: A Corpus of Multi-Perspective News Editorials

1 code implementation NAACL 2021 Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth

We propose MultiOpEd, an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery.

Multi-Task Learning

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System

1 code implementation NAACL 2021 Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, D{\'\i}dac Sur{\'\i}s, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).

Coreference Resolution Event Extraction

Learning to Decompose and Organize Complex Tasks

1 code implementation NAACL 2021 Yi Zhang, Sujay Kumar Jauhar, Julia Kiseleva, Ryen White, Dan Roth

Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.

Generalization in Instruction Following Systems

no code implementations NAACL 2021 Soham Dan, Michael Zhou, Dan Roth

Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence.

Data Augmentation

Event Time Extraction and Propagation via Graph Attention Networks

1 code implementation NAACL 2021 Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, Dan Roth

Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.

Graph Attention Natural Language Understanding +1

Weighted Training for Cross-Task Learning

1 code implementation ICLR 2022 Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, Weijie J. Su

In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks.

Chunking named-entity-recognition +5

Toward Code Generation: A Survey and Lessons from Semantic Parsing

no code implementations26 Apr 2021 Celine Lee, Justin Gottschlich, Dan Roth

With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages.

Code Generation Natural Language Processing +2

Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection

no code implementations NAACL 2021 Sihao Chen, Fan Zhang, Kazoo Sone, Dan Roth

Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context.

Abstractive Text Summarization

Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema

no code implementations EMNLP 2021 Yanai Elazar, Hongming Zhang, Yoav Goldberg, Dan Roth

To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation.

Bias Detection Disentanglement +1

ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning

1 code implementation16 Apr 2021 Rujun Han, I-Hung Hsu, Jiao Sun, Julia Baylon, Qiang Ning, Dan Roth, Nanyun Peng

While these tasks partially evaluate machines' ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.

Machine Reading Comprehension Question Answering

Learning to Reason for Text Generation from Scientific Tables

1 code implementation16 Apr 2021 Nafise Sadat Moosavi, Andreas Rücklé, Dan Roth, Iryna Gurevych

In this paper, we introduce SciGen, a new challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their corresponding descriptions.

Arithmetic Reasoning Data-to-Text Generation

Paired Examples as Indirect Supervision in Latent Decision Models

no code implementations EMNLP 2021 Nitish Gupta, Sameer Singh, Matt Gardner, Dan Roth

Such an objective does not require external supervision for the values of the latent output, or even the end task, yet provides an additional training signal to that provided by individual training examples themselves.

Out-of-Distribution Generalization Question Answering +1

How Good (really) are Grammatical Error Correction Systems?

no code implementations EACL 2021 Alla Rozovskaya, Dan Roth

Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go.

Grammatical Error Correction

A Statistical Analysis of Summarization Evaluation Metrics using Resampling Methods

1 code implementation31 Mar 2021 Daniel Deutsch, Rotem Dror, Dan Roth

After evaluating which of the proposed methods is most appropriate for summarization through two simulation experiments, we analyze the results of applying these methods to several different automatic evaluation metrics across three sets of human annotations.

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

1 code implementation6 Jan 2021 Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, Jonathan Berant

A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly.

Question Answering

Coreference Reasoning in Machine Reading Comprehension

1 code implementation ACL 2021 Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, Iryna Gurevych

We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set.

Coreference Resolution Machine Reading Comprehension +2

Unsupervised Label-aware Event Trigger and Argument Classification

no code implementations30 Dec 2020 Hongming Zhang, Haoyu Wang, Dan Roth

Rather than relying on annotated data, our model matches the semantics of identified events with those of event type labels.

Classification Event Extraction +2

Learning Contextual Causality from Time-consecutive Images

1 code implementation13 Dec 2020 Hongming Zhang, Yintong Huo, Xinran Zhao, Yangqiu Song, Dan Roth

Compared with pure text-based approaches, learning causality from the visual signal has the following advantages: (1) Causality knowledge belongs to the commonsense knowledge, which is rarely expressed in the text but rich in videos; (2) Most events in the video are naturally time-ordered, which provides a rich resource for us to mine causality knowledge from; (3) All the objects in the video can be used as context to study the contextual property of causal relations.

QANom: Question-Answer driven SRL for Nominalizations

1 code implementation COLING 2020 Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Stepanov, Hangfeng He, Dan Roth, Luke Zettlemoyer, Ido Dagan

We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom.

What do we expect from Multiple-choice QA Systems?

no code implementations Findings of the Association for Computational Linguistics 2020 Krunal Shah, Nitish Gupta, Dan Roth

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities.

Multiple-choice Multiple Choice Question Answering (MCQA)

What Are You Trying to Do? Semantic Typing of Event Processes

no code implementations CONLL 2020 Muhao Chen, Hongming Zhang, Haoyu Wang, Dan Roth

This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect.

Learning-To-Rank

Temporal Reasoning on Implicit Events from Distant Supervision

no code implementations NAACL 2021 Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, Dan Roth

We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it.

Natural Language Inference

Pairwise Representation Learning for Event Coreference

no code implementations24 Oct 2020 Xiaodong Yu, Wenpeng Yin, Dan Roth

Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets.

Coreference Resolution Event Cross-Document Coreference Resolution +2

Understanding the Extent to which Summarization Evaluation Metrics Measure the Information Quality of Summaries

1 code implementation23 Oct 2020 Daniel Deutsch, Dan Roth

Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference.

Analogous Process Structure Induction for Sub-event Sequence Prediction

no code implementations EMNLP 2020 Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song, Dan Roth

Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories.

"What Are You Trying to Do?" Semantic Typing of Event Processes

no code implementations13 Oct 2020 Muhao Chen, Hongming Zhang, Haoyu Wang, Dan Roth

This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given an event process, attempts to infer free-form type labels describing (i) the type of action made by the process and (ii) the type of object the process seeks to affect.

Learning-To-Rank

Joint Constrained Learning for Event-Event Relation Extraction

no code implementations EMNLP 2020 Haoyu Wang, Muhao Chen, Hongming Zhang, Dan Roth

Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other.

Relation Extraction

"I'd rather just go to bed": Understanding Indirect Answers

no code implementations7 Oct 2020 Annie Louis, Dan Roth, Filip Radlinski

We revisit a pragmatic inference problem in dialog: understanding indirect responses to questions.

Language Modelling Transfer Learning

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior

1 code implementation Findings of the Association for Computational Linguistics 2020 Zi Lin, Jeremiah Zhe Liu, Zi Yang, Nan Hua, Dan Roth

Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero.

Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary

2 code implementations1 Oct 2020 Daniel Deutsch, Tania Bedrax-Weiss, Dan Roth

A desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with a reference.

Question Answering

Visual Pivoting for (Unsupervised) Entity Alignment

1 code implementation28 Sep 2020 Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier

This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs).

Entity Alignment Knowledge Graphs

From Spatial Relations to Spatial Configurations

no code implementations LREC 2020 Soham Dan, Parisa Kordjamshidi, Julia Bonn, Archna Bhatia, Jon Cai, Martha Palmer, Dan Roth

To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with the fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.

Natural Language Understanding

Understanding Spatial Relations through Multiple Modalities

no code implementations LREC 2020 Soham Dan, Hangfeng He, Dan Roth

Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general.

Common Sense Reasoning

SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics

1 code implementation EMNLP (NLPOSS) 2020 Daniel Deutsch, Dan Roth

We present SacreROUGE, an open-source library for using and developing summarization evaluation metrics.

Commonsense Reasoning for Natural Language Processing

no code implementations ACL 2020 Maarten Sap, Vered Shwartz, Antoine Bosselut, Yejin Choi, Dan Roth

We organize this tutorial to provide researchers with the critical foundations and recent advances in commonsense representation and reasoning, in the hopes of casting a brighter light on this promising area of future research.

Natural Language Processing Pretrained Language Models

``Who said it, and Why?'' Provenance for Natural Language Claims

no code implementations ACL 2020 Yi Zhang, Zachary Ives, Dan Roth

In an era where generating content and publishing it is so easy, we are bombarded with information and are exposed to all kinds of claims, some of which do not always rank high on the truth scale.

Natural Language Inference

Building Low-Resource NER Models Using Non-Speaker Annotation

no code implementations17 Jun 2020 Tatiana Tsygankova, Francesca Marini, Stephen Mayhew, Dan Roth

In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.

Low Resource Named Entity Recognition named-entity-recognition +2

Learnability with Indirect Supervision Signals

no code implementations NeurIPS 2020 Kaifu Wang, Qiang Ning, Dan Roth

Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly.

Generalization Bounds Multi-class Classification

Foreseeing the Benefits of Incidental Supervision

2 code implementations EMNLP 2021 Hangfeng He, Mingyuan Zhang, Qiang Ning, Dan Roth

Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.

Informativeness Learning Theory +3

Incidental Supervision: Moving beyond Supervised Learning

no code implementations25 May 2020 Dan Roth

Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it.

Text Classification with Few Examples using Controlled Generalization

no code implementations NAACL 2019 Abhijit Mahabal, Jason Baldridge, Burcu Karagol Ayan, Vincent Perot, Dan Roth

Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems.

Classification General Classification +2

Context-Based Quotation Recommendation

no code implementations17 May 2020 Ansel MacLaughlin, Tao Chen, Burcu Karagol Ayan, Dan Roth

Our experiments confirm the strong performance of BERT-based methods on this task, which outperform bag-of-words and neural ranking baselines by more than 30% relative across all ranking metrics.

Open-Domain Question Answering

Design Challenges in Low-resource Cross-lingual Entity Linking

1 code implementation EMNLP 2020 Xingyu Fu, Weijia Shi, Xiaodong Yu, Zian Zhao, Dan Roth

Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques.

Cross-Lingual Entity Linking Entity Linking

TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

no code implementations EMNLP 2020 Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, Dan Roth

A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated.

Machine Reading Comprehension Question Answering

Cross-lingual Entity Alignment with Incidental Supervision

1 code implementation EACL 2021 Muhao Chen, Weijia Shi, Ben Zhou, Dan Roth

Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-world object.

Entity Alignment Knowledge Graphs

TransOMCS: From Linguistic Graphs to Commonsense Knowledge

1 code implementation1 May 2020 Hongming Zhang, Daniel Khashabi, Yangqiu Song, Dan Roth

Commonsense knowledge acquisition is a key problem for artificial intelligence.

Cross-Lingual Ability of Multilingual BERT: An Empirical Study

no code implementations ICLR 2020 Karthikeyan K, Zihan Wang, Stephen Mayhew, Dan Roth

Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data.

named-entity-recognition Named Entity Recognition +1

Robust Named Entity Recognition with Truecasing Pretraining

no code implementations15 Dec 2019 Stephen Mayhew, Nitish Gupta, Dan Roth

Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data.

named-entity-recognition NER

Neural Module Networks for Reasoning over Text

2 code implementations ICLR 2020 Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, Matt Gardner

Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations.

Inductive Bias

Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks

no code implementations CL 2019 Mrinmaya Sachan, Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing

At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.

Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses

1 code implementation ACL 2020 Erfan Sadeqi Azer, Daniel Khashabi, Ashish Sabharwal, Dan Roth

Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues.

Bayesian Inference Natural Language Processing

Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization

no code implementations IJCNLP 2019 Daniel Deutsch, Dan Roth

A key challenge in topic-focused summarization is determining what information should be included in the summary, a problem known as content selection.

Named Entity Recognition with Partially Annotated Training Data

no code implementations CONLL 2019 Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai, Dan Roth

Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated.

named-entity-recognition NER

An Improved Neural Baseline for Temporal Relation Extraction

no code implementations IJCNLP 2019 Qiang Ning, Sanjay Subramanian, Dan Roth

Determining temporal relations (e. g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data.

Common Sense Reasoning Natural Language Understanding +2

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

4 code implementations IJCNLP 2019 Wenpeng Yin, Jamaal Hay, Dan Roth

0Shot-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e. g., topic, emotion, event, etc.)

Classification General Classification +2

Improving Generalization in Coreference Resolution via Adversarial Training

no code implementations SEMEVAL 2019 Sanjay Subramanian, Dan Roth

In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text.

Coreference Resolution

BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer

no code implementations WS 2019 Tatiana Tsygankova, Stephen Mayhew, Dan Roth

This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop.

Multilingual Named Entity Recognition named-entity-recognition +2

Solving Hard Coreference Problems

no code implementations HLT 2015 Haoruo Peng, Daniel Khashabi, Dan Roth

Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions.

Coreference Resolution Decision Making +1

Zero-Shot Open Entity Typing as Type-Compatible Grounding

1 code implementation EMNLP 2018 Ben Zhou, Daniel Khashabi, Chen-Tse Tsai, Dan Roth

We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain.

Entity Typing NER

Evidence-based Trustworthiness

no code implementations ACL 2019 Yi Zhang, Zachary Ives, Dan Roth

This paper develops a general framework for estimating the trustworthiness of information sources in an environment where multiple sources provide claims and supporting evidence, and each claim can potentially be produced by multiple sources.

Information Retrieval

Declarative Learning-Based Programming as an Interface to AI Systems

no code implementations18 Jun 2019 Parisa Kordjamshidi, Dan Roth, Kristian Kersting

Data-driven approaches are becoming more common as problem-solving techniques in many areas of research and industry.

Partial Or Complete, That's The Question

no code implementations NAACL 2019 Qiang Ning, Hangfeng He, Chuchu Fan, Dan Roth

For many structured learning tasks, the data annotation process is complex and costly.

CogCompTime: A Tool for Understanding Time in Natural Language Text

no code implementations12 Jun 2019 Qiang Ning, Ben Zhou, Zhili Feng, Haoruo Peng, Dan Roth

Automatic extraction of temporal information in text is an important component of natural language understanding.

Natural Language Understanding

Joint Reasoning for Temporal and Causal Relations

no code implementations ACL 2018 Qiang Ning, Zhili Feng, Hao Wu, Dan Roth

Understanding temporal and causal relations between events is a fundamental natural language understanding task.

Natural Language Understanding

PerspectroScope: A Window to the World of Diverse Perspectives

1 code implementation ACL 2019 Sihao Chen, Daniel Khashabi, Chris Callison-Burch, Dan Roth

This work presents PerspectroScope, a web-based system which lets users query a discussion-worthy natural language claim, and extract and visualize various perspectives in support or against the claim, along with evidence supporting each perspective.

Natural Language Inference Natural Language Understanding

Question Answering as Global Reasoning over Semantic Abstractions

1 code implementation9 Jun 2019 Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Dan Roth

We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions.

Information Retrieval Multiple-choice +1

Evaluation of named entity coreference

no code implementations WS 2019 Oshin Agarwal, Sanjay Subramanian, Ani Nenkova, Dan Roth

It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name.

Coreference Resolution

ner and pos when nothing is capitalized

no code implementations IJCNLP 2019 Stephen Mayhew, Tatiana Tsygankova, Dan Roth

While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.

Machine Translation named-entity-recognition +4

Grammar Error Correction in Morphologically Rich Languages: The Case of Russian

no code implementations TACL 2019 Alla Rozovskaya, Dan Roth

Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are available.

On the Possibilities and Limitations of Multi-hop Reasoning Under Linguistic Imperfections

no code implementations8 Jan 2019 Daniel Khashabi, Erfan Sadeqi Azer, Tushar Khot, Ashish Sabharwal, Dan Roth

The idea is to consider two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic space that captures a noisy grounding of the meaning space in the words of a language---the level at which all systems, whether neural or symbolic, operate.

Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems

no code implementations NeurIPS 2018 Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing

Finally, we also show how Nuts&Bolts can be used to achieve improvements on a relation extraction task and on the end task of answering Newtonian physics problems.

Relation Extraction

Discourse in Multimedia: A Case Study in Information Extraction

no code implementations13 Nov 2018 Mrinmaya Sachan, Kumar Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing

At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.

Named Person Coreference in English News

no code implementations26 Oct 2018 Oshin Agarwal, Sanjay Subramanian, Ani Nenkova, Dan Roth

Here, we evaluate two state of the art coreference resolution systems on the subtask of Named Person Coreference, in which we are interested in identifying a person mentioned by name, along with all other mentions of the person, by pronoun or generic noun phrase.

Coreference Resolution named-entity-recognition +1

Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages

1 code implementation EMNLP 2018 Shyam Upadhyay, Jordan Kodner, Dan Roth

Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction.

Entity Linking Transliteration

Joint Multilingual Supervision for Cross-lingual Entity Linking

1 code implementation EMNLP 2018 Shyam Upadhyay, Nitish Gupta, Dan Roth

This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language.

Cross-Lingual Entity Linking Entity Linking

TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification

1 code implementation EMNLP 2018 Wenpeng Yin, Dan Roth

We develop TwoWingOS (two-wing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence.

Natural Language Inference

A Distributional and Orthographic Aggregation Model for English Derivational Morphology

1 code implementation ACL 2018 Daniel Deutsch, John Hewitt, Dan Roth

Modeling derivational morphology to generate words with particular semantics is useful in many text generation tasks, such as machine translation or abstractive question answering.

abstractive question answering Machine Translation +3

TALEN: Tool for Annotation of Low-resource ENtities

1 code implementation ACL 2018 Stephen Mayhew, Dan Roth

We present a new web-based interface, TALEN, designed for named entity annotation in low-resource settings where the annotators do not speak the language.

Named Entity Recognition

Term Definitions Help Hypernymy Detection

no code implementations SEMEVAL 2018 Wenpeng Yin, Dan Roth

Existing methods of hypernymy detection mainly rely on statistics over a big corpus, either mining some co-occurring patterns like "animals such as cats" or embedding words of interest into context-aware vectors.

Exploiting Partially Annotated Data in Temporal Relation Extraction

no code implementations SEMEVAL 2018 Qiang Ning, Zhongzhi Yu, Chuchu Fan, Dan Roth

As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.

Relation Extraction

A Multi-Axis Annotation Scheme for Event Temporal Relations

no code implementations ACL 2018 Qiang Ning, Hao Wu, Dan Roth

Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition.

Preference-Guided Planning: An Active Elicitation Approach

no code implementations19 Apr 2018 Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao, Doppa, Dan Roth, Sriraam Natarajan

Planning with preferences has been employed extensively to quickly generate high-quality plans.

Exploiting Partially Annotated Data for Temporal Relation Extraction

no code implementations18 Apr 2018 Qiang Ning, Zhongzhi Yu, Chuchu Fan, Dan Roth

As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.

Relation Extraction

Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource

no code implementations NAACL 2018 Qiang Ning, Hao Wu, Haoruo Peng, Dan Roth

We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow.

Relation Extraction

Robust Cross-lingual Hypernymy Detection using Dependency Context

1 code implementation NAACL 2018 Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat, Dan Roth

We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts.

Natural Language Inference Word Embeddings

Mapping to Declarative Knowledge for Word Problem Solving

1 code implementation TACL 2018 Subhro Roy, Dan Roth

Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc.

Translation

Adapting to Learner Errors with Minimal Supervision

no code implementations CL 2017 Alla Rozovskaya, Dan Roth, Mark Sammons

This article considers the problem of correcting errors made by English as a Second Language writers from a machine learning perspective, and addresses an important issue of developing an appropriate training paradigm for the task, one that accounts for error patterns of non-native writers using minimal supervision.

Entity Linking via Joint Encoding of Types, Descriptions, and Context

no code implementations EMNLP 2017 Nitish Gupta, Sameer Singh, Dan Roth

For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge.

Entity Linking

Cheap Translation for Cross-Lingual Named Entity Recognition

no code implementations EMNLP 2017 Stephen Mayhew, Chen-Tse Tsai, Dan Roth

Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e. g., the availability of Wikipedia in the target language.

Cross-Lingual NER named-entity-recognition +2

Story Comprehension for Predicting What Happens Next

no code implementations EMNLP 2017 Snigdha Chaturvedi, Haoruo Peng, Dan Roth

Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense.

Common Sense Reasoning Natural Language Understanding +3

Learning What is Essential in Questions

1 code implementation CONLL 2017 Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Dan Roth

Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains.

Information Retrieval Question Answering +1

A Joint Model for Semantic Sequences: Frames, Entities, Sentiments

no code implementations CONLL 2017 Haoruo Peng, Snigdha Chaturvedi, Dan Roth

Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task.

Cloze Test Discourse Parsing +2