Search Results for author: Swabha Swayamdipta

Found 29 papers, 18 papers with code

Investigating the Benefits of Free-Form Rationales

no code implementations25 May 2022 Jiao Sun, Swabha Swayamdipta, Jonathan May, Xuezhe Ma

After controlling for instances where rationales leak the correct answer while not providing additional background knowledge, we find that incorporating only 5% of rationales during training can boost model performance by 47. 22% for CoS-E and 57. 14% for ECQA during inference.

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

1 code implementation16 Jan 2022 Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi

Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns.

Natural Language Inference Text Generation

Reframing Human-AI Collaboration for Generating Free-Text Explanations

1 code implementation16 Dec 2021 Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, Yejin Choi

We create a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop.

Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

no code implementations15 Nov 2021 Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. Smith

The perceived toxicity of language can vary based on someone's identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in dataset and model biases.

Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information

1 code implementation16 Oct 2021 Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta

However, this comparison provides little understanding of how difficult each instance in a given distribution is, or what attributes make the dataset difficult for a given model.

Sister Help: Data Augmentation for Frame-Semantic Role Labeling

1 code implementation EMNLP (LAW, DMR) 2021 Ayush Pancholy, Miriam R. L. Petruck, Swabha Swayamdipta

While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet.

Data Augmentation Natural Language Processing +2

Contrastive Explanations for Model Interpretability

1 code implementation EMNLP 2021 Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, Yoav Goldberg

Our method is based on projecting model representation to a latent space that captures only the features that are useful (to the model) to differentiate two potential decisions.

Text Classification

MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers

3 code implementations NeurIPS 2021 Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaid Harchaoui

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem.

Text Generation

Challenges in Automated Debiasing for Toxic Language Detection

2 code implementations EACL 2021 Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Noah A. Smith, Yejin Choi

Overall, our findings show that debiasing a model trained on biased toxic language data is not as effective as simply relabeling the data to remove existing biases.

Fairness Text Classification

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

5 code implementations EMNLP 2020 Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi

Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.

Out-of-Distribution Generalization

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

1 code implementation ACL 2020 Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. Smith

Our method presents a favorable speed/accuracy tradeoff in almost all cases, producing models which are up to five times faster than the state of the art, while preserving their accuracy.

Natural Language Inference Text Classification

Adversarial Filters of Dataset Biases

1 code implementation ICML 2020 Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi

Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples.

Natural Language Inference

Shallow Syntax in Deep Water

no code implementations29 Aug 2019 Swabha Swayamdipta, Matthew Peters, Brendan Roof, Chris Dyer, Noah A. Smith

Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain.

Transfer Learning in Natural Language Processing

no code implementations NAACL 2019 Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, Thomas Wolf

The classic supervised machine learning paradigm is based on learning in isolation, a single predictive model for a task using a single dataset.

Natural Language Processing Transfer Learning +1

Syntactic Scaffolds for Semantic Structures

1 code implementation EMNLP 2018 Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, Noah A. Smith

We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks.

Coreference Resolution

Polyglot Semantic Role Labeling

no code implementations ACL 2018 Phoebe Mulcaire, Swabha Swayamdipta, Noah Smith

Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages.

Dependency Parsing Semantic Dependency Parsing +1

Learning Joint Semantic Parsers from Disjoint Data

2 code implementations NAACL 2018 Hao Peng, Sam Thomson, Swabha Swayamdipta, Noah A. Smith

We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap.

Dependency Parsing Semantic Dependency Parsing

Annotation Artifacts in Natural Language Inference Data

no code implementations NAACL 2018 Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, Noah A. Smith

Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to.

Natural Language Inference Text Categorization

Multi-Mention Learning for Reading Comprehension with Neural Cascades

no code implementations ICLR 2018 Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski

Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur.

Reading Comprehension

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

9 code implementations29 Jun 2017 Swabha Swayamdipta, Sam Thomson, Chris Dyer, Noah A. Smith

We present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates.

Semantic Parsing

DyNet: The Dynamic Neural Network Toolkit

4 code implementations15 Jan 2017 Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin

In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.

graph construction

Cannot find the paper you are looking for? You can Submit a new open access paper.