Papers by Helen Yannakoudakis
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses (2020.emnlp-main)
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| Challenge: | CWEB is a new benchmark for grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Approach: | They propose to broaden the target domain of grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Outcome: | The proposed model can't rely on a strong internal language model in low error density domains. |
Joint Modelling of Emotion and Abusive Language Detection (2020.acl-main)
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| Challenge: | Existing methods for abuse detection focus on linguistic properties of comments and online communities of users, disregarding the emotional state of the users and how this might affect their language. |
| Approach: | They propose to combine emotion and abusive language detection to create a multi-task learning framework that allows one task to inform the other. |
| Outcome: | The proposed model improves on the previous models, incorporating affective features into the learning framework. |
Scientific and Creative Analogies in Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing analogy datasets focus on a limited set of analogical relations with a high similarity of the two domains between which the analogy holds. |
| Approach: | They propose a dataset that encodes analogy in pretrained language models . they use a system that maps attributes and relational structures across dissimilar domains . |
| Outcome: | The proposed dataset shows that state-of-the-art models achieve low performance on analogy tasks . |
A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors (N19-1)
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| Challenge: | a recent study shows that neural sequential labelers overfit their training data to detect SVA errors. |
| Approach: | They propose a simple protocol that generates a neural sequential labeler from silver standard data and gold standard data. |
| Outcome: | The proposed method leads to more robust detection of SVA errors on silver standard data and gold standard data. |
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. |
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification (N19-1)
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| Challenge: | Current approaches to metaphor identification use restricted linguistic contexts, e.g. by only considering a verb’s arguments or the sentence containing a phrase. |
| Approach: | They propose to train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods. |
| Outcome: | The proposed classifiers obtained state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without complex metaphor-specific features or deep neural architectures employed by other systems. |
Prompting open-source and commercial language models for grammatical error correction of English learner text (2024.findings-acl)
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Christopher Davis, Andrew Caines, O Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
| Challenge: | Recent advances in generative AI have enabled us to prompt large language models (LLMs) to produce texts which are fluent and grammatical. |
| Approach: | They evaluate model performance by measuring their performance on established benchmarks. |
| Outcome: | The proposed models outperform supervised English GEC models on fluency correction benchmarks and commercial LLMs on edit benchmarks. |
KidsArtBench: Multi-Dimensional Children’s Art Evaluation with Attribute-Aware MLLMs (2026.eacl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) show impressive capabilities across visual–language tasks, but their capacity to evaluate artistic expression remains limited. |
| Approach: | They propose an attribute-specific multi-LoRA approach where each attribute corresponds to a distinct evaluation dimension in the scoring rubric. |
| Outcome: | The proposed approach increases correlation from 0.468 to 0.653 on Qwen2.5-VL-7B, with the largest gains on perceptual dimensions and narrowed gaps on higher-order attributes. |
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions (2020.acl-main)
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| Challenge: | a growing demand for the ability to communicate in English means automated tutoring and assessment systems are becoming more popular. |
| Approach: | They propose to use automatic speech recognition transcripts to grade spontaneous speech based on textual features. |
| Outcome: | The proposed system improves on a transformer encoder with native language identification as an auxiliary task. |
A Survey of Cognitive Distortion Detection and Classification in NLP (2025.findings-emnlp)
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| Challenge: | despite momentum in natural language processing, the field remains fragmented . inconsistencies in CD taxonomies, task formulations and evaluation practices limit comparability . |
| Approach: | This review provides a comprehensive review of 38 studies spanning two decades . they map how CDs have been implemented in computational research and evaluate the methods applied. |
| Outcome: | The paper presents the first comprehensive review of 38 studies spanning two decades . it summarises common task setups and highlights persistent challenges to support more coherent research. |
A Study of LLMs’ Preferences for Libraries and Programming Languages (2026.findings-acl)
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| Challenge: | Existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use. |
| Approach: | They conduct the first systematic study of LLMs’ preferences for libraries and programming languages when generating code, covering eight different LLM. |
| Outcome: | The proposed benchmarks show that LLMs prioritize familiarity and popularity over suitability and task-specific optimality. |
Logging Keystrokes in Writing by English Learners (2024.lrec-main)
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Georgios Velentzas, Andrew Caines, Rita Borgo, Erin Pacquetet, Clive Hamilton, Taylor Arnold, Diane Nicholls, Paula Buttery, Thomas Gaillat, Nicolas Ballier, Helen Yannakoudakis
| Challenge: | Essay writing is a skill commonly taught and practised in schools. |
| Approach: | They collect and analyse data representing the essay writing process from start to finish by recording every keystroke from multiple writers participating in the study. |
| Outcome: | The data collected from 1,006 writers is compared against a standard dataset of texts, keystroke logs and metadata for public release. |
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. |
Ruddit: Norms of Offensiveness for English Reddit Comments (2021.acl-long)
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| Challenge: | Existing methods to detect offensive language have been limited by categorical labels . however, there are several challenges in the detection of such content . |
| Approach: | They analyze Reddit comments with fine-grained, real-valued offensiveness scores . they evaluate the ability of widely-used neural models to predict offensiveness . |
| Outcome: | The proposed method produces highly reliable offensiveness scores and can predict scores on reddit comments. |
Author Profiling for Abuse Detection (C18-1)
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| Challenge: | Existing methods for detecting abusive content rely on textual cues and lexical cue information. |
| Approach: | They propose a method that incorporates community-based profiling features of Twitter users to detect abusive content by using a dataset of 16k tweets. |
| Outcome: | The proposed approach outperforms the current state-of-the-art in abuse detection on a dataset of 16k tweets. |
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension (2023.findings-eacl)
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| Challenge: | Existing frameworks for referring expression comprehension with commonsense knowledge are lacking in the field of multimodal referring . |
| Approach: | They propose a framework for commonsense knowledge Enhanced Transformers which integrates commonsensible knowledge into representations of objects in an image. |
| Outcome: | The proposed framework improves on the existing state of the art in referring expression comprehension with commonsense knowledge (CK-Transformer) it achieves 3.14% accuracy over the existing framework. |
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing (2022.acl-long)
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Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
| Challenge: | Meta-learning can help overcome resource scarcity in cross-lingual NLP problems . pre-training of models requires large annotated training sets for the task at hand . |
| Approach: | They propose to use meta-learning to train a model to learn a parameter initialization that can adapt quickly to new languages. |
| Outcome: | The proposed model-agnostic meta-learning improves on language transfer and standard supervised learning baselines for unseen, typologically diverse, and low-resource languages in a few-shot learning setup. |
K-hop neighbourhood regularization for few-shot learning on graphs: A case study of text classification (2023.eacl-main)
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| Challenge: | We show that few-sample word-document graphs can be used for improved learning in low-resource settings. |
| Approach: | They propose a method to utilize word-document graph properties for improved learning in low-resource settings by using a regularizer for heterogeneous graphs. |
| Outcome: | The proposed method outperforms a baseline TextGCN with 17% accuracy over eight languages while performing on par with the state-of-the-art models. |
Few-Shot Open-Set Classification via Reasoning-Aware Decomposition (2025.emnlp-main)
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| Challenge: | Large language models excel at few-shot learning, but their ability to reject out-of-distribution examples remains under-explored. |
| Approach: | They introduce a novel amortised Generative Flow Network framework that uses latent trajectories to approximate the Bayesian posterior. |
| Outcome: | The proposed framework can generalise with as few as 4 examples per class, enabling Llama 3.2 3B to achieve up to 80% of the performance of Llma 3.3 70B in complex datasets. |
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection (2024.findings-naacl)
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| Challenge: | despite the performance of community models for malicious content detection, misinformation and hate speech continue to propagate on social media networks. |
| Approach: | They propose a new evaluation setup for community models for malicious content detection based on a few-shot subgraph sampling approach to test generalisation of models using local explorations of a larger graph. |
| Outcome: | The proposed evaluation setup outperforms existing models on real-world graphs on a training graph. |
Abusive Language Detection with Graph Convolutional Networks (N19-1)
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| Challenge: | Existing approaches to abusive language detection only capture shallow properties of online communities . a new approach captures both the structure of online community and linguistic behavior of users . |
| Approach: | They propose a graph convolutional network approach that captures the linguistic behavior of users . they propose to model homophily by embeddings for authors that encode the structure of their communities . |
| Outcome: | The proposed approach captures both the structure and linguistic behavior of users in online communities . authors show that the proposed approach significantly advances the current state of the art . |
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability (2021.findings-emnlp)
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| Challenge: | Abuse on the Internet is an important societal problem of our time. |
| Approach: | They propose to use user and community information to enhance detection of abusive language . they propose to propose properties that an explainable method should aim to exhibit . |
| Outcome: | The proposed methods leverage user and community information to enhance detection of abusive language. |
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation (2020.findings-emnlp)
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| Challenge: | Existing methods for word sense disambiguation (WSD) are limited and require large datasets annotated with word senses. |
| Approach: | They propose a meta-learning framework for few-shot word sense disambiguation where the goal is to learn to disambiguate unseen words from only a few labeled instances. |
| Outcome: | The proposed framework is based on a large training dataset and a small number of examples. |
Multilingual and cross-lingual document classification: A meta-learning approach (2021.eacl-main)
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| Challenge: | Existing methods to document classification in low-resource languages are under-resourced . 6% of the world's languages are spoken, and many have inadequate resources . |
| Approach: | They propose a meta-learning approach to document classification in low-resource languages . they propose 'nuclear-shot' cross-lingual adaptation to previously unseen languages based on limited data . |
| Outcome: | The proposed method performs on-par on some languages while under-resourced in others. |