3.2k citations · 7k across the 61 of their papers we have counts for
70 papers
Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing
Freda Shi, Kevin Gimpel, Karen Livescu
We present the structured average intersection-over-union ratio (STRUCT-IOU), a similarity metric between constituency parse trees motivated by the problem of evaluating speech par…
Audio-Visual Neural Syntax Acquisition
Cheng-I Jeff Lai, Freda Shi, Puyuan Peng +10
We study phrase structure induction from visually-grounded speech. The core idea is to first segment the speech waveform into sequences of word segments, and subsequently induce ph…
The Benefits of Label-Description Training for Zero-Shot Text Classification
Lingyu Gao, Debanjan Ghosh, Kevin Gimpel
Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data in order to classify among specific lab…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
"What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation
Lingyu Gao, Debanjan Ghosh, Kevin Gimpel
We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and fi…
Reconsidering the Past: Optimizing Hidden States in Language Models
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. Similar to dynamic evaluation (K…
Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing
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…
On Generalization in Coreference Resolution
Shubham Toshniwal, Patrick Xia, Sam Wiseman +2
While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains. We consolidate a…
TVStoryGen: A Dataset for Generating Stories with Character Descriptions
Mingda Chen, Kevin Gimpel
We introduce TVStoryGen, a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters i…
Paraphrastic Representations at Scale
John Wieting, Kevin Gimpel, Graham Neubig +1
We present a system that allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. We also release trained models for Englis…
SummScreen: A Dataset for Abstractive Screenplay Summarization
Mingda Chen, Zewei Chu, Sam Wiseman +1
We introduce SummScreen, a summarization dataset comprised of pairs of TV series transcripts and human written recaps. The dataset provides a challenging testbed for abstractive su…
Chess as a Testbed for Language Model State Tracking
Shubham Toshniwal, Sam Wiseman, Karen Livescu +1
Transformer language models have made tremendous strides in natural language understanding tasks. However, the complexity of natural language makes it challenging to ascertain how…
Substructure Substitution: Structured Data Augmentation for NLP
Haoyue Shi, Karen Livescu, Kevin Gimpel
We study a family of data augmentation methods, substructure substitution (SUB2), for natural language processing (NLP) tasks. SUB2 generates new examples by substituting substruct…
Unsupervised Label Refinement Improves Dataless Text Classification
Zewei Chu, Karl Stratos, Kevin Gimpel
Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising…
WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections
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. In this work, we cast generating…
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
Shubham Toshniwal, Sam Wiseman, Allyson Ettinger +2
Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models. Recent work doing incremental coreference resolu…
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference
Xiaoan Ding, Tianyu Liu, Baobao Chang +2
While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In th…
On the Role of Supervision in Unsupervised Constituency Parsing
Haoyue Shi, Karen Livescu, Kevin Gimpel
We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing score on the Wall Street Journal (WSJ) development set (1,700…
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks
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. Researchers are…
Mining Knowledge for Natural Language Inference from Wikipedia Categories
Mingda Chen, Zewei Chu, Karl Stratos +1
Accurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations. To alleviate the need for labeled data, we introduce Wik…
Deep Clustering of Text Representations for Supervision-free Probing of Syntax
Vikram Gupta, Haoyue Shi, Kevin Gimpel +1
We explore deep clustering of text representations for unsupervised model interpretation and induction of syntax. As these representations are high-dimensional, out-of-the-box meth…
Exemplar-Controllable Paraphrasing and Translation using Bitext
Mingda Chen, Sam Wiseman, Kevin Gimpel
Most prior work on exemplar-based syntactically controlled paraphrase generation relies on automatically-constructed large-scale paraphrase datasets, which are costly to create. We…
NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources
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. NatCat consists of document-category…
Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size
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. While the results from these models are impressive, applying t…