Search Results for author: Mark Dras

Found 48 papers, 9 papers with code

Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations

1 code implementation RepL4NLP (ACL) 2022 Na Liu, Mark Dras, Wei Emma Zhang

Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.

Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis

no code implementations18 Mar 2022 Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.

reinforcement-learning Representation Learning

Mention Flags (MF): Constraining Transformer-based Text Generators

1 code implementation ACL 2021 YuFei Wang, Ian Wood, Stephen Wan, Mark Dras, Mark Johnson

In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder.

Common Sense Reasoning Text Generation

Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation

no code implementations NeurIPS 2021 YuFei Wang, Can Xu, Huang Hu, Chongyang Tao, Stephen Wan, Mark Dras, Mark Johnson, Daxin Jiang

Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e. g., BART and T5), have exhibited compelling performance on various natural language generation tasks.

Text Generation

Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection

1 code implementation5 Jun 2020 Omid Mohamad Nezami, Akshay Chaturvedi, Mark Dras, Utpal Garain

We specifically aim to attack the widely used Faster R-CNN by changing the predicted label for a particular object in an image: where prior work has targeted one specific object (a stop sign), we generalise to arbitrary objects, with the key challenge being the need to change the labels of all bounding boxes for all instances of that object type.

Adversarial Attack Image Captioning +3

Towards Generating Stylized Image Captions via Adversarial Training

no code implementations8 Aug 2019 Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris, Len Hamey

While most image captioning aims to generate objective descriptions of images, the last few years have seen work on generating visually grounded image captions which have a specific style (e. g., incorporating positive or negative sentiment).

Image Captioning

Image Captioning using Facial Expression and Attention

no code implementations8 Aug 2019 Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris

An analysis of the generated captions finds that, perhaps unexpectedly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.

Image Captioning Natural Language Processing

Generalised Differential Privacy for Text Document Processing

no code implementations26 Nov 2018 Natasha Fernandes, Mark Dras, Annabelle McIver

We address the problem of how to "obfuscate" texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text.

General Classification Semantic Similarity +1

Senti-Attend: Image Captioning using Sentiment and Attention

no code implementations24 Nov 2018 Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris

However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption.

Image Captioning

Native Language Identification With Classifier Stacking and Ensembles

no code implementations CL 2018 Shervin Malmasi, Mark Dras

Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art.

General Classification Language Acquisition +2

Automatic Recognition of Student Engagement using Deep Learning and Facial Expression

3 code implementations7 Aug 2018 Omid Mohamad Nezami, Mark Dras, Len Hamey, Deborah Richards, Stephen Wan, Cecile Paris

This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.

Facial Expression Recognition

Face-Cap: Image Captioning using Facial Expression Analysis

1 code implementation6 Jul 2018 Omid Mohamad Nezami, Mark Dras, Peter Anderson, Len Hamey

In this work, we present two variants of our Face-Cap model, which embed facial expression features in different ways, to generate image captions.

Image Captioning

Predicting accuracy on large datasets from smaller pilot data

no code implementations ACL 2018 Mark Johnson, Peter Anderson, Mark Dras, Mark Steedman

Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from models trained on a small pilot training dataset.

Document Classification

Author Obfuscation Using Generalised Differential Privacy

no code implementations22 May 2018 Natasha Fernandes, Mark Dras, Annabelle McIver

The problem of obfuscating the authorship of a text document has received little attention in the literature to date.

Cryptography and Security

From Word Segmentation to POS Tagging for Vietnamese

1 code implementation ALTA 2017 Dat Quoc Nguyen, Thanh Vu, Dai Quoc Nguyen, Mark Dras, Mark Johnson

This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable.

Part-Of-Speech Tagging POS +1

Unsupervised Text Segmentation Based on Native Language Characteristics

no code implementations ACL 2017 Shervin Malmasi, Mark Dras, Mark Johnson, Lan Du, Magdalena Wolska

Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language.

Text Segmentation

Native Language Identification using Stacked Generalization

no code implementations19 Mar 2017 Shervin Malmasi, Mark Dras

Ensemble methods using multiple classifiers have proven to be the most successful approach for the task of Native Language Identification (NLI), achieving the current state of the art.

Native Language Identification

An empirical study for Vietnamese dependency parsing

no code implementations ALTA 2016 Dat Quoc Nguyen, Mark Dras, Mark Johnson

This paper presents an empirical comparison of different dependency parsers for Vietnamese, which has some unusual characteristics such as copula drop and verb serialization.

Dependency Parsing

Modeling Language Change in Historical Corpora: The Case of Portuguese

no code implementations LREC 2016 Marcos Zampieri, Shervin Malmasi, Mark Dras

This paper presents a number of experiments to model changes in a historical Portuguese corpus composed of literary texts for the purpose of temporal text classification.

General Classification POS +1

Irish Treebanking and Parsing: A Preliminary Evaluation

no code implementations LREC 2012 Teresa Lynn, {\"O}zlem {\c{C}}etino{\u{g}}lu, Jennifer Foster, Elaine U{\'\i} Dhonnchadha, Mark Dras, Josef van Genabith

This paper describes the early stages in the development of new language resources for Irish ― namely the first Irish dependency treebank and the first Irish statistical dependency parser.

Machine Translation POS

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