1 code implementation • EMNLP 2021 • Quintin Pope, Xiaoli Z. Fern
We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification.
no code implementations • 19 Jan 2021 • Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin
This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes.
no code implementations • ACL 2020 • Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Prasad Tadepalli
Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences.
no code implementations • 14 Aug 2019 • Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Rasha Obeidat, Prasad Tadepalli
We present a new local entity disambiguation system.
1 code implementation • NAACL 2019 • Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, Prasad Tadepalli
Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances.
no code implementations • 9 Sep 2018 • Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli
We also study the behavior of the proposed model to provide explanations for the model's decisions.
no code implementations • EMNLP 2018 • Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli
Deep learning models have achieved remarkable success in natural language inference (NLI) tasks.
no code implementations • COLING 2018 • Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Chao Ma, Rasha Obeidat, Prasad Tadepalli
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS).
no code implementations • COLING 2018 • Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, Prasad Tadepalli
We present a novel deep learning architecture to address the cloze-style question answering task.
no code implementations • NAACL 2018 • Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Z. Fern, Oladimeji Farri
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on Natural Language Inference on SNLI
no code implementations • ACL 2016 • Reza Ghaeini, Xiaoli Z. Fern, Liang Huang, Prasad Tadepalli
Traditional event detection methods heavily rely on manually engineered rich features.
no code implementations • 5 Feb 2018 • Zeyu You, Raviv Raich, Xiaoli Z. Fern, Jinsub Kim
The performance of the proposed model is demonstrated on both synthetic and real-world data.
no code implementations • 7 Mar 2016 • Behrouz Behmardi, Forrest Briggs, Xiaoli Z. Fern, Raviv Raich
In this approach each bag is represented as a distribution using the principle of ME.
no code implementations • 30 Dec 2014 • Yuanli Pei, Xiaoli Z. Fern, Rómer Rosales, Teresa Vania Tjahja
Experimental results demonstrate: 1) the usefulness of relative constraints, in particular when don't know answers are considered; 2) the improved performance of the proposed method over state-of-the-art methods that utilize either relative or pairwise constraints; and 3) the robustness of our method in the presence of noisy constraints, such as those provided by human judgement.
no code implementations • 14 Nov 2014 • Anh T. Pham, Raviv Raich, Xiaoli Z. Fern
To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label.
no code implementations • 15 Sep 2014 • Sicheng Xiong, Rómer Rosales, Yuanli Pei, Xiaoli Z. Fern
This work focuses on active learning of distance metrics from relative comparison information.
no code implementations • 25 Nov 2013 • Qi Lou, Raviv Raich, Forrest Briggs, Xiaoli Z. Fern
Contrary to the common assumption in MIML that each instance in a bag belongs to one of the known classes, in novelty detection, we focus on the scenario where bags may contain novel-class instances.
no code implementations • 22 Apr 2013 • Forrest Briggs, Xiaoli Z. Fern, Jed Irvine
Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of different species.
no code implementations • NeurIPS 2011 • Javad Azimi, Alan Fern, Xiaoli Z. Fern
This paper defines a novel problem formulation with the following important extensions: 1) allowing for concurrent experiments; 2) allowing for stochastic experiment durations; and 3) placing constraints on both the total number of experiments and the total experimental time.
no code implementations • NeurIPS 2011 • Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern
However, unlike standard approaches to missing data, in this setting we know that facts are more likely to be missing from the text in cases where the reader can infer them from the facts that are mentioned combined with the domain knowledge.
no code implementations • NeurIPS 2010 • Javad Azimi, Alan Fern, Xiaoli Z. Fern
Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate.