b'Kevin Gimpel'

research

∙ 05/03/2023

The Benefits of Label-Description Training for Zero-Shot Text Classification

Large language models have improved zero-shot text classification by all...

0 Lingyu Gao, et al. ∙

research

∙ 05/17/2022

"What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation

We propose a type-controlled framework for inquisitive question generati...

0 Lingyu Gao, et al. ∙

research

∙ 12/16/2021

Reconsidering the Past: Optimizing Hidden States in Language Models

We present Hidden-State Optimization (HSO), a gradient-based method for ...

0 Davis Yoshida, et al. ∙

research

∙ 10/16/2021

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

We present substructure distribution projection (SubDP), a technique tha...

0 Haoyue Shi, et al. ∙

research

∙ 09/20/2021

On Generalization in Coreference Resolution

While coreference resolution is defined independently of dataset domain,...

5 Shubham Toshniwal, et al. ∙

research

∙ 09/18/2021

TVRecap: A Dataset for Generating Stories with Character Descriptions

We introduce TVRecap, a story generation dataset that requires generatin...

0 Mingda Chen, et al. ∙

research

∙ 04/30/2021

Paraphrastic Representations at Scale

We present a system that allows users to train their own state-of-the-ar...

0 John Wieting, et al. ∙

research

∙ 04/14/2021

SummScreen: A Dataset for Abstractive Screenplay Summarization

We introduce SummScreen, a summarization dataset comprised of pairs of T...

0 Mingda Chen, et al. ∙

research

∙ 02/26/2021

Learning Chess Blindfolded: Evaluating Language Models on State Tracking

Transformer language models have made tremendous strides in natural lang...

5 Shubham Toshniwal, et al. ∙

research

∙ 01/02/2021

Substructure Substitution: Structured Data Augmentation for NLP

We study a family of data augmentation methods, substructure substitutio...

0 Haoyue Shi, et al. ∙

research

∙ 12/29/2020

Generating Wikipedia Article Sections from Diverse Data Sources

Datasets for data-to-text generation typically focus either on multi-dom...

0 Mingda Chen, et al. ∙

research

∙ 12/08/2020

Unsupervised Label Refinement Improves Dataless Text Classification

Dataless text classification is capable of classifying documents into pr...

0 Zewei Chu, et al. ∙

research

∙ 10/24/2020

Clustering Contextualized Representations of Text for Unsupervised Syntax Induction

We explore clustering of contextualized text representations for two uns...

0 Vikram Gupta, et al. ∙

research

∙ 10/12/2020

Controllable Paraphrasing and Translation with a Syntactic Exemplar

Most prior work on exemplar-based syntactically controlled paraphrase ge...

0 Mingda Chen, et al. ∙

research

∙ 10/08/2020

Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference

While discriminative neural network classifiers are generally preferred,...

0 Xiaoan Ding, et al. ∙

research

∙ 10/06/2020

Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks

Long document coreference resolution remains a challenging task due to t...

0 Shubham Toshniwal, et al. ∙

research

∙ 10/06/2020

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

Many tasks in natural language processing involve predicting structured ...

0 Lifu Tu, et al. ∙

research

∙ 10/06/2020

On the Role of Supervision in Unsupervised Constituency Parsing

We analyze several recent unsupervised constituency parsing models, whic...

0 Haoyue Shi, et al. ∙

research

∙ 10/03/2020

Mining Knowledge for Natural Language Inference from Wikipedia Categories

Accurate lexical entailment (LE) and natural language inference (NLI) of...

0 Mingda Chen, et al. ∙

research

∙ 09/29/2020

Natcat: Weakly Supervised Text Classification with Naturally Annotated Datasets

We seek to improve text classification by leveraging naturally annotated...

0 Zewei Chu, et al. ∙

research

∙ 08/16/2020

Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size

Fine-tuning a pretrained transformer for a downstream task has become a ...

0 Davis Yoshida, et al. ∙

research

∙ 06/06/2020

A Cross-Task Analysis of Text Span Representations

Many natural language processing (NLP) tasks involve reasoning with text...

0 Shubham Toshniwal, et al. ∙

research

∙ 05/16/2020

Learning Probabilistic Sentence Representations from Paraphrases

Probabilistic word embeddings have shown effectiveness in capturing noti...

0 Mingda Chen, et al. ∙

research

∙ 05/06/2020

PeTra: A Sparsely Supervised Memory Model for People Tracking

We propose PeTra, a memory-augmented neural network designed to track en...

0 Shubham Toshniwal, et al. ∙

research

∙ 05/02/2020

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

We propose to train a non-autoregressive machine translation model to mi...

0 Lifu Tu, et al. ∙

research

∙ 11/21/2019

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

We present a large-scale dataset for the task of rewriting an ill-formed...

0 Zewei Chu, et al. ∙

research

∙ 11/07/2019

Improving Joint Training of Inference Networks and Structured Prediction Energy Networks

Deep energy-based models are powerful, but pose challenges for learning ...

0 Lifu Tu, et al. ∙

research

∙ 10/01/2019

Latent-Variable Generative Models for Data-Efficient Text Classification

Generative classifiers offer potential advantages over their discriminat...

0 Xiaoan Ding, et al. ∙

research

∙ 09/30/2019

Simple and Effective Paraphrastic Similarity from Parallel Translations

We present a model and methodology for learning paraphrastic sentence em...

0 John Wieting, et al. ∙

research

∙ 09/30/2019

Generating Diverse Story Continuations with Controllable Semantics

We propose a simple and effective modeling framework for controlled gene...

4 Lifu Tu, et al. ∙

research

∙ 09/26/2019

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Increasing model size when pretraining natural language representations ...

11 Zhenzhong Lan, et al. ∙

research

∙ 09/14/2019

Beyond BLEU: Training Neural Machine Translation with Semantic Similarity

While most neural machine translation (NMT) systems are still trained us...

0 John Wieting, et al. ∙

research

∙ 08/31/2019

Evaluation Benchmarks and Learning Criteriafor Discourse-Aware Sentence Representations

Prior work on pretrained sentence embeddings and benchmarks focus on the...

0 Mingda Chen, et al. ∙

research

∙ 08/31/2019

EntEval: A Holistic Evaluation Benchmark for Entity Representations

Rich entity representations are useful for a wide class of problems invo...

0 Mingda Chen, et al. ∙

research

∙ 06/23/2019

Variational Sequential Labelers for Semi-Supervised Learning

We introduce a family of multitask variational methods for semi-supervis...

0 Mingda Chen, et al. ∙

research

∙ 06/23/2019

Smaller Text Classifiers with Discriminative Cluster Embeddings

Word embedding parameters often dominate overall model sizes in neural m...

0 Mingda Chen, et al. ∙

research

∙ 06/07/2019

Visually Grounded Neural Syntax Acquisition

We present the Visually Grounded Neural Syntax Learner (VG-NSL), an appr...

2 Haoyue Shi, et al. ∙

research

∙ 06/03/2019

Controllable Paraphrase Generation with a Syntactic Exemplar

Prior work on controllable text generation usually assumes that the cont...

0 Mingda Chen, et al. ∙

research

∙ 04/05/2019

PoMo: Generating Entity-Specific Post-Modifiers in Context

We introduce entity post-modifier generation as an instance of a collabo...

0 Jun Seok Kang, et al. ∙

research

∙ 04/02/2019

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

We propose a generative model for a sentence that uses two latent variab...

0 Mingda Chen, et al. ∙

research

∙ 04/01/2019

Benchmarking Approximate Inference Methods for Neural Structured Prediction

Exact structured inference with neural network scoring functions is comp...

6 Lifu Tu, et al. ∙

research

∙ 10/28/2018

Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora

We consider the problem of automatically generating textual paraphrases ...

0 Yuanzhe Pang, et al. ∙

research

∙ 04/17/2018

Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

We propose syntactically controlled paraphrase networks (SCPNs) and use ...

0 Mohit Iyyer, et al. ∙

research

∙ 03/09/2018

Learning Approximate Inference Networks for Structured Prediction

Structured prediction energy networks (SPENs; Belanger & McCallum 2016) ...

0 Lifu Tu, et al. ∙

research

∙ 02/14/2018

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

The growing importance of massive datasets with the advent of deep learn...

0 Dan Hendrycks, et al. ∙

research

∙ 11/15/2017

Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations

We extend the work of Wieting et al. (2017), back-translating a large pa...

0 John Wieting, et al. ∙

research

∙ 10/28/2017

A Study of All-Convolutional Encoders for Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a popular sequence predic...

0 Kalpesh Krishna, et al. ∙

research

∙ 08/01/2017

End-to-End Neural Segmental Models for Speech Recognition

Segmental models are an alternative to frame-based models for sequence p...

0 Hao Tang, et al. ∙

research

∙ 06/09/2017

Learning to Embed Words in Context for Syntactic Tasks

We present models for embedding words in the context of surrounding word...

0 Lifu Tu, et al. ∙

research

∙ 06/06/2017

Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

We consider the problem of learning general-purpose, paraphrastic senten...

0 John Wieting, et al. ∙

Kevin Gimpel

Featured Co-authors