Swabha Swayamdipta

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Carnegie Mellon University ∙ University of Washington ∙ Allen Institute for Artificial Intelligence

Adversarial Filters of Dataset Biases

Large neural models have demonstrated human-level performance on languag...
02/10/2020 ∙ by Ronan Le Bras, et al. ∙ 14 ∙ share

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Multi-Mention Learning for Reading Comprehension with Neural Cascades

Reading comprehension is a challenging task, especially when executed ac...
11/02/2017 ∙ by Swabha Swayamdipta, et al. ∙ 0 ∙ share

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Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold

We present a new, efficient frame-semantic parser that labels semantic a...
06/29/2017 ∙ by Swabha Swayamdipta, et al. ∙ 0 ∙ share

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Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

We present a transition-based parser that jointly produces syntactic and...
06/29/2016 ∙ by Swabha Swayamdipta, et al. ∙ 0 ∙ share

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Annotation Artifacts in Natural Language Inference Data

Large-scale datasets for natural language inference are created by prese...
03/06/2018 ∙ by Suchin Gururangan, et al. ∙ 0 ∙ share

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Learning Joint Semantic Parsers from Disjoint Data

We present a new approach to learning semantic parsers from multiple dat...
04/17/2018 ∙ by Hao Peng, et al. ∙ 0 ∙ share

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Polyglot Semantic Role Labeling

Previous approaches to multilingual semantic dependency parsing treat la...
05/29/2018 ∙ by Phoebe Mulcaire, et al. ∙ 0 ∙ share

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Syntactic Scaffolds for Semantic Structures

We introduce the syntactic scaffold, an approach to incorporating syntac...
08/30/2018 ∙ by Swabha Swayamdipta, et al. ∙ 0 ∙ share

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DyNet: The Dynamic Neural Network Toolkit

We describe DyNet, a toolkit for implementing neural network models base...
01/15/2017 ∙ by Graham Neubig, et al. ∙ 0 ∙ share

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Shallow Syntax in Deep Water

Shallow syntax provides an approximation of phrase-syntactic structure o...
08/29/2019 ∙ by Swabha Swayamdipta, et al. ∙ 0 ∙ share

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The Right Tool for the Job: Matching Model and Instance Complexities

As NLP models become larger, executing a trained model requires signific...
04/16/2020 ∙ by Roy Schwartz, et al. ∙ 0 ∙ share

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G-DAUG: Generative Data Augmentation for Commonsense Reasoning

Recent advances in commonsense reasoning depend on large-scale human-ann...
04/24/2020 ∙ by Yiben Yang, et al. ∙ 0 ∙ share

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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Language models pretrained on text from a wide variety of sources form t...
04/23/2020 ∙ by Suchin Gururangan, et al. ∙ 0 ∙ share

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Swabha Swayamdipta

Featured Co-authors

Graham Neubig

Noah A. Smith

Chris Dyer

Yoav Goldberg

Luke Zettlemoyer

Yejin Choi

Trevor Cohn

Samuel R. Bowman

Hao Peng

Kevin Duh

Omer Levy

Adversarial Filters of Dataset Biases

Multi-Mention Learning for Reading Comprehension with Neural Cascades

Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold

Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

Annotation Artifacts in Natural Language Inference Data

Learning Joint Semantic Parsers from Disjoint Data

Polyglot Semantic Role Labeling

Syntactic Scaffolds for Semantic Structures

DyNet: The Dynamic Neural Network Toolkit

Shallow Syntax in Deep Water

The Right Tool for the Job: Matching Model and Instance Complexities

G-DAUG: Generative Data Augmentation for Commonsense Reasoning

Don't Stop Pretraining: Adapt Language Models to Domains and Tasks