no code implementations • ACL (ECNLP) 2021 • Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
no code implementations • 22 Mar 2024 • I-Hung Hsu, Zihan Xue, Nilay Pochh, Sahil Bansal, Premkumar Natarajan, Jayanth Srinivasa, Nanyun Peng
Event linking connects event mentions in text with relevant nodes in a knowledge base (KB).
1 code implementation • 16 Nov 2023 • Kuan-Hao Huang, I-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Premkumar Natarajan, Kai-Wei Chang, Nanyun Peng, Heng Ji
In this work, we identify and address evaluation challenges, including inconsistency due to varying data assumptions or preprocessing steps, the insufficiency of current evaluation frameworks that may introduce dataset or data split bias, and the low reproducibility of some previous approaches.
1 code implementation • 25 May 2022 • I-Hung Hsu, Kuan-Hao Huang, Shuning Zhang, Wenxin Cheng, Premkumar Natarajan, Kai-Wei Chang, Nanyun Peng
In this work, we propose to take a unified view of all these tasks and introduce TAGPRIME to address relational structure extraction problems.
1 code implementation • ACL 2022 • Kuan-Hao Huang, I-Hung Hsu, Premkumar Natarajan, Kai-Wei Chang, Nanyun Peng
We present a study on leveraging multilingual pre-trained generative language models for zero-shot cross-lingual event argument extraction (EAE).
1 code implementation • ACL 2021 • Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng
Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner.
no code implementations • 6 Apr 2021 • Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan
For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.
no code implementations • 1 Jan 2021 • I-Hung Hsu, Xiao Guo, Premkumar Natarajan, Nanyun Peng
The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for discourse-level relation extraction (DRE) tasks.
no code implementations • 28 Nov 2020 • Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Premkumar Natarajan
Finally, we propose (1) baseline methods and (2) a new adversarial learning framework for class-agnostic detection that forces the model to exclude class-specific information from features used for predictions.
Ranked #100 on
Image Classification
on ObjectNet
(using extra training data)
1 code implementation • 24 Oct 2020 • Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng
Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining.
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng
We present a general approach towards controllable societal biases in natural language generation (NLG).
no code implementations • 2 Dec 2019 • Ayush Jaiswal, Rob Brekelmans, Daniel Moyer, Greg Ver Steeg, Wael Abd-Almageed, Premkumar Natarajan
Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization.
no code implementations • 11 Nov 2019 • Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg, Wael Abd-Almageed, Premkumar Natarajan
We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism.
1 code implementation • IJCNLP 2019 • Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng
We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups.
1 code implementation • 7 Jul 2019 • I-Hung Hsu, Ayush Jaiswal, Premkumar Natarajan
Deep neural network models for speech recognition have achieved great success recently, but they can learn incorrect associations between the target and nuisance factors of speech (e. g., speaker identities, background noise, etc.
no code implementations • 7 May 2019 • Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan
We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions.
2 code implementations • CVPR 2019 • Jiaxin Cheng, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc.
1 code implementation • CVPR 2019 • Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Iacopo Masi, Premkumar Natarajan
Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda.
no code implementations • NeurIPS 2018 • Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan
Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting.
1 code implementation • 17 Feb 2018 • Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan
We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models.
no code implementations • 20 Nov 2017 • Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan
Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$).
no code implementations • 6 Jul 2017 • Ayush Jaiswal, Ekraam Sabir, Wael Abd-Almageed, Premkumar Natarajan
In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages.
no code implementations • 4 May 2015 • Yue Wu, Stephen Rawls, Wael Abd-Almageed, Premkumar Natarajan
In this paper we present a fully trainable binarization solution for degraded document images.
no code implementations • 18 May 2013 • Yue Wu, Brian Tracey, Premkumar Natarajan, Joseph P. Noonan
In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising.