Search Results for author: Kevin Gimpel

Found 89 papers, 36 papers with code

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

Reconsidering the Past: Optimizing Hidden States in Language Models

no code implementations Findings (EMNLP) 2021 Davis Yoshida, Kevin Gimpel

We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time.

Language Modelling

Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing

no code implementations ACL 2022 Haoyue Shi, Kevin Gimpel, Karen Livescu

We present substructure distribution projection (SubDP), a technique that projects a distribution over structures in one domain to another, by projecting substructure distributions separately.

Dependency Parsing

On Generalization in Coreference Resolution

1 code implementation CRAC (ACL) 2021 Shubham Toshniwal, Patrick Xia, Sam Wiseman, Karen Livescu, Kevin Gimpel

While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains.

Coreference Resolution Data Augmentation

TVRecap: A Dataset for Generating Stories with Character Descriptions

1 code implementation18 Sep 2021 Mingda Chen, Kevin Gimpel

We introduce TVRecap, a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved.

Abstractive Text Summarization Story Generation

Paraphrastic Representations at Scale

1 code implementation30 Apr 2021 John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-Kirkpatrick

We train these models on large amounts of data, achieving significantly improved performance from the original papers proposing the methods on a suite of monolingual semantic similarity, cross-lingual semantic similarity, and bitext mining tasks.

Semantic Similarity Semantic Textual Similarity

Learning Chess Blindfolded

no code implementations1 Jan 2021 Shubham Toshniwal, Sam Wiseman, Karen Livescu, Kevin Gimpel

Motivated by this issue, we consider the task of language modeling for the game of chess.

Game of Chess Language Modelling +1

Adding Recurrence to Pretrained Transformers

no code implementations1 Jan 2021 Davis Yoshida, Allyson Ettinger, Kevin Gimpel

Fine-tuning a pretrained transformer for a downstream task has become a standard method in NLP in the last few years.

Language Modelling

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

1 code implementation Findings (ACL) 2021 Mingda Chen, Sam Wiseman, Kevin Gimpel

Datasets for data-to-text generation typically focus either on multi-domain, single-sentence generation or on single-domain, long-form generation.

Data-to-Text Generation

Unsupervised Label Refinement Improves Dataless Text Classification

1 code implementation Findings (ACL) 2021 Zewei Chu, Karl Stratos, Kevin Gimpel

This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice.

Classification General Classification +1

Deep Clustering of Text Representations for Supervision-free Probing of Syntax

no code implementations24 Oct 2020 Vikram Gupta, Haoyue Shi, Kevin Gimpel, Mrinmaya Sachan

We explore deep clustering of text representations for unsupervised model interpretation and induction of syntax.

Deep Clustering TAG

Exemplar-Controllable Paraphrasing and Translation using Bitext

1 code implementation12 Oct 2020 Mingda Chen, Sam Wiseman, Kevin Gimpel

Our experimental results show that our models achieve competitive results on controlled paraphrase generation and strong performance on controlled machine translation.

Machine Translation Paraphrase Generation +1

Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference

1 code implementation EMNLP 2020 Xiaoan Ding, Tianyu Liu, Baobao Chang, Zhifang Sui, Kevin Gimpel

We explore training objectives for discriminative fine-tuning of our generative classifiers, showing improvements over log loss fine-tuning from prior work .

Natural Language Inference

An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks

1 code implementation EMNLP 2020 Lifu Tu, Tianyu Liu, Kevin Gimpel

Many tasks in natural language processing involve predicting structured outputs, e. g., sequence labeling, semantic role labeling, parsing, and machine translation.

Machine Translation Natural Language Processing +3

On the Role of Supervision in Unsupervised Constituency Parsing

no code implementations EMNLP 2020 Haoyue Shi, Karen Livescu, Kevin Gimpel

We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing $F_1$ score on the Wall Street Journal (WSJ) development set (1, 700 sentences).

Constituency Parsing Data Augmentation +1

NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources

1 code implementation AKBC 2021 Zewei Chu, Karl Stratos, Kevin Gimpel

We describe NatCat, a large-scale resource for text classification constructed from three data sources: Wikipedia, Stack Exchange, and Reddit.

Classification General Classification +1

Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size

no code implementations16 Aug 2020 Davis Yoshida, Allyson Ettinger, Kevin Gimpel

Fine-tuning a pretrained transformer for a downstream task has become a standard method in NLP in the last few years.

Language Modelling

Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners

no code implementations WS 2020 Lingyu Gao, Kevin Gimpel, Arnar Jensson

Simple features of the distractor and correct answer correlate with the annotations, though we find substantial benefit to additionally using large-scale pretrained models to measure the fit of the distractor in the context.

Multiple-choice

A Cross-Task Analysis of Text Span Representations

1 code implementation WS 2020 Shubham Toshniwal, Haoyue Shi, Bowen Shi, Lingyu Gao, Karen Livescu, Kevin Gimpel

Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution.

Coreference Resolution Natural Language Processing +1

Learning Probabilistic Sentence Representations from Paraphrases

no code implementations WS 2020 Mingda Chen, Kevin Gimpel

Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation for sentences.

Word Embeddings

ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation

1 code implementation ACL 2020 Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman, Kevin Gimpel

We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model.

Machine Translation Translation

How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions

1 code implementation21 Nov 2019 Zewei Chu, Mingda Chen, Jing Chen, Miaosen Wang, Kevin Gimpel, Manaal Faruqui, Xiance Si

We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one.

Question Rewriting

Generating Diverse Story Continuations with Controllable Semantics

no code implementations WS 2019 Lifu Tu, Xiaoan Ding, Dong Yu, Kevin Gimpel

We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs.

Simple and Effective Paraphrastic Similarity from Parallel Translations

4 code implementations ACL 2019 John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-Kirkpatrick

We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating paraphrase corpora.

Sentence Embeddings

Beyond BLEU: Training Neural Machine Translation with Semantic Similarity

1 code implementation14 Sep 2019 John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, Graham Neubig

While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy.

Machine Translation Semantic Similarity +2

Beyond BLEU:Training Neural Machine Translation with Semantic Similarity

no code implementations ACL 2019 John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, Graham Neubig

While most neural machine translation (NMT)systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can significantly improve final translation accuracy.

Machine Translation Semantic Similarity +2

Smaller Text Classifiers with Discriminative Cluster Embeddings

1 code implementation NAACL 2018 Mingda Chen, Kevin Gimpel

Word embedding parameters often dominate overall model sizes in neural methods for natural language processing.

Natural Language Processing

Visually Grounded Neural Syntax Acquisition

no code implementations ACL 2019 Haoyue Shi, Jiayuan Mao, Kevin Gimpel, Karen Livescu

We define concreteness of constituents by their matching scores with images, and use it to guide the parsing of text.

Visual Grounding

Controllable Paraphrase Generation with a Syntactic Exemplar

no code implementations ACL 2019 Mingda Chen, Qingming Tang, Sam Wiseman, Kevin Gimpel

Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori.

Paraphrase Generation Representation Learning

PoMo: Generating Entity-Specific Post-Modifiers in Context

no code implementations NAACL 2019 Jun Seok Kang, Robert L. Logan IV, Zewei Chu, Yang Chen, Dheeru Dua, Kevin Gimpel, Sameer Singh, Niranjan Balasubramanian

Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity.

A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations

1 code implementation NAACL 2019 Mingda Chen, Qingming Tang, Sam Wiseman, Kevin Gimpel

We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics.

Disentanglement Semantic Similarity +1

Benchmarking Approximate Inference Methods for Neural Structured Prediction

1 code implementation NAACL 2019 Lifu Tu, Kevin Gimpel

One approach is to perform gradient descent with respect to the output structure directly (Belanger and McCallum, 2016).

Structured Prediction

Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer

no code implementations WS 2019 Richard Yuanzhe Pang, Kevin Gimpel

We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic preservation and fluency as well as a way to combine them into a single overall score.

Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

2 code implementations NAACL 2018 Mohit Iyyer, John Wieting, Kevin Gimpel, Luke Zettlemoyer

We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples.

Learning Approximate Inference Networks for Structured Prediction

3 code implementations ICLR 2018 Lifu Tu, Kevin Gimpel

Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables and then optimizing the energy with respect to them.

Language Modelling Multi-Label Classification +2

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

1 code implementation NeurIPS 2018 Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel

We utilize trusted data by proposing a loss correction technique that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers.

Data Poisoning Natural Language Processing

A Study of All-Convolutional Encoders for Connectionist Temporal Classification

no code implementations28 Oct 2017 Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu

We explore whether deep convolutional neural networks (CNNs) can be used effectively instead of RNNs as the "encoder" in CTC.

Automatic Speech Recognition Classification +2

End-to-End Neural Segmental Models for Speech Recognition

no code implementations1 Aug 2017 Hao Tang, Liang Lu, Lingpeng Kong, Kevin Gimpel, Karen Livescu, Chris Dyer, Noah A. Smith, Steve Renals

Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at a time.

speech-recognition Speech Recognition

Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

no code implementations EMNLP 2017 John Wieting, Jonathan Mallinson, Kevin Gimpel

We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b).

Machine Translation Sentence Embeddings +1

Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

no code implementations ACL 2017 John Wieting, Kevin Gimpel

We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b).

Sentence Embeddings Transfer Learning

Constraints Based Convex Belief Propagation

no code implementations NeurIPS 2016 YAniv Tenzer, Alex Schwing, Kevin Gimpel, Tamir Hazan

Inference in Markov random fields subject to consistency structure is a fundamental problem that arises in many real-life applications.

Emergent Predication Structure in Hidden State Vectors of Neural Readers

no code implementations WS 2017 Hai Wang, Takeshi Onishi, Kevin Gimpel, David Mcallester

A significant number of neural architectures for reading comprehension have recently been developed and evaluated on large cloze-style datasets.

Reading Comprehension

Broad Context Language Modeling as Reading Comprehension

no code implementations EACL 2017 Zewei Chu, Hai Wang, Kevin Gimpel, David Mcallester

Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015).

Coreference Resolution Language Modelling +1

End-to-End Training Approaches for Discriminative Segmental Models

no code implementations21 Oct 2016 Hao Tang, Weiran Wang, Kevin Gimpel, Karen Livescu

Similarly to hybrid HMM-neural network models, segmental models of this class can be trained in two stages (frame classifier training followed by linear segmental model weight training), end to end (joint training of both frame classifier and linear weights), or with end-to-end fine-tuning after two-stage training.

speech-recognition Speech Recognition

Who did What: A Large-Scale Person-Centered Cloze Dataset

no code implementations EMNLP 2016 Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, David Mcallester

We have constructed a new "Who-did-What" dataset of over 200, 000 fill-in-the-gap (cloze) multiple choice reading comprehension problems constructed from the LDC English Gigaword newswire corpus.

Multiple-choice Reading Comprehension

Efficient Segmental Cascades for Speech Recognition

no code implementations2 Aug 2016 Hao Tang, Weiran Wang, Kevin Gimpel, Karen Livescu

Discriminative segmental models offer a way to incorporate flexible feature functions into speech recognition.

speech-recognition Speech Recognition

Early Methods for Detecting Adversarial Images

1 code implementation1 Aug 2016 Dan Hendrycks, Kevin Gimpel

Many machine learning classifiers are vulnerable to adversarial perturbations.

Charagram: Embedding Words and Sentences via Character n-grams

no code implementations EMNLP 2016 John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu

We present Charagram embeddings, a simple approach for learning character-based compositional models to embed textual sequences.

Part-Of-Speech Tagging Sentence Similarity +1

Adjusting for Dropout Variance in Batch Normalization and Weight Initialization

1 code implementation8 Jul 2016 Dan Hendrycks, Kevin Gimpel

We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy.

Data Augmentation

Gaussian Error Linear Units (GELUs)

7 code implementations27 Jun 2016 Dan Hendrycks, Kevin Gimpel

We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function.

Computer Vision Natural Language Processing

Towards Universal Paraphrastic Sentence Embeddings

no code implementations25 Nov 2015 John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu

We again find that the word averaging models perform well for sentence similarity and entailment, outperforming LSTMs.

General Classification Sentence Embeddings +2

Discriminative Segmental Cascades for Feature-Rich Phone Recognition

no code implementations22 Jul 2015 Hao Tang, Weiran Wang, Kevin Gimpel, Karen Livescu

A typical solution is to use approximate decoding, either by beam pruning in a single pass or by beam pruning to generate a lattice followed by a second pass.

speech-recognition Speech Recognition +1

From Paraphrase Database to Compositional Paraphrase Model and Back

1 code implementation TACL 2015 John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu, Dan Roth

The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates.

Word Embeddings

Predicting the NFL using Twitter

no code implementations25 Oct 2013 Shiladitya Sinha, Chris Dyer, Kevin Gimpel, Noah A. Smith

We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics.

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