Jennifer Biggs


2023

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Task and Sentiment Adaptation for Appraisal Tagging
Lin Tian | Xiuzhen Zhang | Myung Hee Kim | Jennifer Biggs
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

The Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions and has contributed to sentiment analysis and opinion mining. As developing appraisal-annotated resources requires tagging of several dimensions with distinct semantic taxonomies, it has been primarily conducted manually by human experts through expensive and time-consuming processes. In this paper, we study how to automatically identify and annotate text segments for appraisal. We formulate the problem as a sequence tagging problem and propose novel task and sentiment adapters based on language models for appraisal tagging. Our model, named Adaptive Appraisal (Aˆ2), achieves superior performance than baseline adapter-based models and other neural classification models, especially for cross-domain and cross-language settings. Source code for Aˆ2 is available at: https://github.com/ltian678/AA-code.git

2022

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Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association
Pradeesh Parameswaran | Jennifer Biggs | David Powers
Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association

2021

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Robustness Analysis of Grover for Machine-Generated News Detection
Rinaldo Gagiano | Maria Myung-Hee Kim | Xiuzhen Zhang | Jennifer Biggs
Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association

Advancements in Natural Language Generation have raised concerns on its potential misuse for deep fake news. Grover is a model for both generation and detection of neural fake news. While its performance on automatically discriminating neural fake news surpassed GPT-2 and BERT, Grover could face a variety of adversarial attacks to deceive detection. In this work, we present an investigation of Grover’s susceptibility to adversarial attacks such as character-level and word-level perturbations. The experiment results show that even a singular character alteration can cause Grover to fail, affecting up to 97% of target articles with unlimited attack attempts, exposing a lack of robustness. We further analyse these misclassified cases to highlight affected words, identify vulnerability within Grover’s encoder, and perform a novel visualisation of cumulative classification scores to assist in interpreting model behaviour.

2015

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Comparison of Visual and Logical Character Segmentation in Tesseract OCR Language Data for Indic Writing Scripts
Jennifer Biggs
Proceedings of the Australasian Language Technology Association Workshop 2015

2014

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OCR and Automated Translation for the Navigation of non-English Handsets: A Feasibility Study with Arabic
Jennifer Biggs | Michael Broughton
Proceedings of the Australasian Language Technology Association Workshop 2014