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20172025
most citedNeural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

3 citations · 3 across the 4 of their papers we have counts for

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cs.CL2025

Conditional Multi-Stage Failure Recovery for Embodied Agents

Youmna Farag, Svetlana Stoyanchev, Mohan Li +2

Embodied agents performing complex tasks are susceptible to execution failures, motivating the need for effective failure recovery mechanisms. In this work, we introduce a conditio…

cs.CL2024

An LLM Feature-based Framework for Dialogue Constructiveness Assessment

Lexin Zhou, Youmna Farag, Andreas Vlachos

Research on dialogue constructiveness assessment focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their pers…

cs.CL2023

Opening up Minds with Argumentative Dialogues

Youmna Farag, Charlotte O. Brand, Jacopo Amidei +4

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. In this work,…

cs.CL2020

Analyzing Neural Discourse Coherence Models

Youmna Farag, Josef Valvoda, Helen Yannakoudakis +1

In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various…

cs.CL2019

Multi-Task Learning for Coherence Modeling

Youmna Farag, Helen Yannakoudakis

We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a hi…

cs.CL2018★ 3 cited

Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

Youmna Farag, Helen Yannakoudakis, Ted Briscoe

We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent…

cs.CL2017

An Error-Oriented Approach to Word Embedding Pre-Training

Youmna Farag, Marek Rei, Ted Briscoe

We propose a novel word embedding pre-training approach that exploits writing errors in learners' scripts. We compare our method to previous models that tune the embeddings based o…