Search Results for author: Sunipa Dev

Found 12 papers, 8 papers with code

Representation Learning for Resource-Constrained Keyphrase Generation

1 code implementation15 Mar 2022 Di wu, Wasi Uddin Ahmad, Sunipa Dev, Kai-Wei Chang

State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data.

Denoising Keyphrase Generation +2

Socially Aware Bias Measurements for Hindi Language Representations

1 code implementation15 Oct 2021 Vijit Malik, Sunipa Dev, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang

Language representations are efficient tools used across NLP applications, but they are strife with encoded societal biases.

What do Bias Measures Measure?

no code implementations7 Aug 2021 Sunipa Dev, Emily Sheng, Jieyu Zhao, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Nanyun Peng, Kai-Wei Chang

To address this gap, this work presents a comprehensive survey of existing bias measures in NLP as a function of the associated NLP tasks, metrics, datasets, and social biases and corresponding harms.

Natural Language Processing

VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations

1 code implementation6 Apr 2021 Archit Rathore, Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei zhang, Bei Wang

To aid this, we present Visualization of Embedding Representations for deBiasing system ("VERB"), an open-source web-based visualization tool that helps the users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties.

Decision Making Dimensionality Reduction +3

The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability

1 code implementation25 Nov 2020 Sunipa Dev

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining.

Knowledge Graphs

OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings

1 code implementation EMNLP 2021 Sunipa Dev, Tao Li, Jeff M. Phillips, Vivek Srikumar

Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks.

Word Embeddings

On Measuring and Mitigating Biased Inferences of Word Embeddings

1 code implementation25 Aug 2019 Sunipa Dev, Tao Li, Jeff Phillips, Vivek Srikumar

Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them.

Natural Language Inference Word Embeddings

Attenuating Bias in Word Vectors

1 code implementation23 Jan 2019 Sunipa Dev, Jeff Phillips

Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages.

Closed Form Word Embedding Alignment

no code implementations4 Jun 2018 Sunipa Dev, Safia Hassan, Jeff M. Phillips

We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e. g., GloVe or word2vec).

Word Embeddings

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