Papers by Youmna Farag
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input (N18-1)
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| Challenge: | Existing approaches to Automated Essay Scoring (AES) are not well-suited to capture adversarially crafted input of grammatical but incoherent sequences of sentences. |
| Approach: | They propose a neural model of local coherence that can effectively learn connectedness features between sentences. |
| Outcome: | The proposed approach strengthens the validity of neural essay scoring models. |
An LLM Feature-based Framework for Dialogue Constructiveness Assessment (2024.emnlp-main)
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| Challenge: | Existing studies on dialogue constructiveness assessment focus on analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness. |
| Approach: | They propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature- and neural approaches while mitigating their downsides. |
| Outcome: | The proposed framework outperforms standard feature-based models and neural models on three dialogue constructiveness datasets. |
Multi-Task Learning for Coherence Modeling (P19-1)
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| Challenge: | Existing models for assessing discourse coherence have been developed for summarization and language assessment. |
| Approach: | They propose a hierarchical neural network that learns to predict a document-level coherence score along with word-level grammatical roles, taking advantage of inductive transfer between the two tasks. |
| Outcome: | The proposed framework can predict document-level coherence score and word-level grammatical roles using inductive transfer between the two tasks. |
Opening up Minds with Argumentative Dialogues (2022.findings-emnlp)
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Youmna Farag, Charlotte Brand, Jacopo Amidei, Paul Piwek, Tom Stafford, Svetlana Stoyanchev, Andreas Vlachos
| Challenge: | Recent research on argumentative dialogues has focused on persuading people to take some action, changing their stance on the topic of discussion, or winning debates. |
| Approach: | They present a dataset of 183 argumentative dialogues about veganism, Brexit and COVID-19 vaccination. |
| Outcome: | The proposed model is significantly better on other dialogue properties such as engagement and clarity. |