Search Results for author: Swabha Swayamdipta

Found 37 papers, 22 papers with code

COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements

no code implementations3 Jun 2023 Xuhui Zhou, Hao Zhu, Akhila Yerukola, Thomas Davidson, Jena D. Hwang, Swabha Swayamdipta, Maarten Sap

To study the contextual dynamics of offensiveness, we train models to generate COBRA explanations, with and without access to the context.

NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge

no code implementations8 May 2023 Phillip Howard, Junlin Wang, Vasudev Lal, Gadi Singer, Yejin Choi, Swabha Swayamdipta

However, access to inference API for such models is limited, thereby restricting the scope and the diversity of the knowledge acquisition.

Knowledge Distillation

We're Afraid Language Models Aren't Modeling Ambiguity

1 code implementation27 Apr 2023 Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, Yejin Choi

We find that the task remains extremely challenging, including for the recent GPT-4, whose generated disambiguations are considered correct only 32% of the time in human evaluation, compared to 90% for disambiguations in our dataset.

MAUVE Scores for Generative Models: Theory and Practice

1 code implementation30 Dec 2022 Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui

We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images.


I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation

no code implementations19 Dec 2022 Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi

Here, we investigate an alternative that a priori seems impossible: can smaller language models (e. g., GPT-2) win over models that are orders of magnitude larger and better (e. g., GPT-3), if powered with novel commonsense distillation algorithms?

Imitation Learning Knowledge Distillation

NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation

1 code implementation22 Oct 2022 Phillip Howard, Gadi Singer, Vasudev Lal, Yejin Choi, Swabha Swayamdipta

While counterfactual data augmentation offers a promising step towards robust generalization in natural language processing, producing a set of counterfactuals that offer valuable inductive bias for models remains a challenge.

Data Augmentation Inductive Bias +3

REV: Information-Theoretic Evaluation of Free-Text Rationales

1 code implementation10 Oct 2022 Hanjie Chen, Faeze Brahman, Xiang Ren, Yangfeng Ji, Yejin Choi, Swabha Swayamdipta

More concretely, we propose a metric called REV (Rationale Evaluation with conditional V-information), to quantify the amount of new, label-relevant information in a rationale beyond the information already available in the input or the label.

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 implementation NAACL 2022 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 implementations NAACL 2022 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 Semantic Parsing +1

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 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 +1

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 +1

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.

Transfer Learning Word Embeddings

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.


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 TriviaQA

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

10 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

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