Papers by Elena Kochkina
Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education (2025.emnlp-main)
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| Challenge: | Existing public terminology datasets for MT research are limited in language coverage or domain specificity, making it difficult to assess or improve MT systems in specialized settings. |
| Approach: | They propose a multilingual terminology resource for tax and financial education covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
| Outcome: | The proposed terminology resource covers seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
All-in-one: Multi-task Learning for Rumour Verification (C18-1)
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| Challenge: | Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline . previous work focused on rumor detection, rumou tracking and stance classification as separate components . |
| Approach: | They propose a multi-task learning approach that allows joint training of main and auxiliary tasks, improving the performance of rumour verification. |
| Outcome: | The proposed approach improves the performance of rumour verification by combining main and auxiliary tasks into one pipeline. |
FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking (2025.findings-naacl)
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| Challenge: | FinNLI is a benchmark dataset for Financial Natural Language Inference (NLI) across diverse financial texts like SEC Filings, Annual Reports, and Earnings Call transcripts. |
| Approach: | They propose to use FinNLI to evaluate financial natural language inference models across diverse financial texts like SEC Filings, Annual Reports, and Earnings Call transcripts. |
| Outcome: | The proposed dataset is based on a high-quality test set of 3,304 instances annotated by finance experts. |
Distill and Align Decomposition for Enhanced Claim Verification (2026.findings-eacl)
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Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero, Arturo Oncevay, Charese Smiley, Xiaomo Liu, Manuela Veloso
| Challenge: | Existing methods for complex claim verification struggle to align decomposition quality with verification performance. |
| Approach: | They propose a reinforcement learning approach that optimizes decomposition quality and verifier alignment using Group Relative Policy Optimization. |
| Outcome: | The proposed method outperforms prompt-based approaches and existing methods in six evaluation settings. |
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims (2022.naacl-main)
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| Challenge: | Existing datasets focus on a single medium, information domain or specific application . authors propose novel methods for automated veracity assessment based on Natural Language Inference . |
| Approach: | They propose to build a PANACEA dataset that combines different data sources with different foci to ensure a unique set of claims. |
| Outcome: | The proposed methods are competitive with SOTA methods and provide a detailed discussion. |
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data (D19-50)
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| Challenge: | Popular NLP tasks such as sentiment analysis and event extraction from social media are examples of imbalanced classification problems. |
| Approach: | They propose a method to generalise on dissimilar training and test data using a measure of similarity between datasets. |
| Outcome: | The proposed method achieves the second highest score on sentence-level propaganda classification. |
Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency (2024.lrec-main)
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Toyin D. Aguda, Suchetha Siddagangappa, Elena Kochkina, Simerjot Kaur, Dongsheng Wang, Charese Smiley
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets, but their effectiveness on domain specific datasets remains under-explored. |
| Approach: | They compare the annotations produced by three LLMs against expert annotators and crowdworkers. |
| Outcome: | The proposed models outperform expert crowdworkers and crowd-sourced annotators on domain specific datasets. |
A Variational Approach for Mitigating Entity Bias in Relation Extraction (2025.acl-short)
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| Challenge: | Relation Extraction (RE) models often rely excessively on entities, resulting in poor generalization. |
| Approach: | They propose a Variational Information Bottleneck (VIB) framework to reduce entity bias in Relation Extraction (RE) . their method extracts relational information from unstructured data to improve generalization . |
| Outcome: | The proposed method achieves state-of-the-art on general and financial domain RE datasets, excelling in in-domain settings and out-of domain. |
Knowledge Graphs for Real-World Rumour Verification (2024.lrec-main)
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| Challenge: | Recent advances in automated rumour verification have limited results in real-world scenarios. |
| Approach: | They propose to use Twitter responses to construct knowledge graphs based on the PHEME dataset to identify discrepancies between the evidence retrieved and PHE ME’s labels. |
| Outcome: | The proposed model outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset. |
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies (2021.eacl-main)
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| Challenge: | Biomedical question-answering (QA) provides users with high-quality information from a vast scientific literature. |
| Approach: | They propose to use a biomedical entity-aware masking strategy to fine-tune masked language models to their domains. |
| Outcome: | The proposed approach is an adaptation process for masked LMs, not memory or components. |
Advanced Messaging Platform (AMP): Pipeline for Automated Enterprise Email Processing (2025.acl-industry)
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Simerjot Kaur, Charese Smiley, Keshav Ramani, Elena Kochkina, Mathieu Sibue, Samuel Mensah, Pietro Totis, Cecilia Tilli, Toyin Aguda, Daniel Borrajo, Manuela Veloso
| Challenge: | a lack of publicly available datasets for training and benchmarking limits current AI techniques' effectiveness in industry-specific applications. |
| Approach: | They propose an email automation pipeline that automates email response generation at scale in real-world enterprise settings. |
| Outcome: | The proposed pipeline automates email response generation at scale in real-world environments. |
Estimating predictive uncertainty for rumour verification models (2020.acl-main)
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| Challenge: | Inability to correctly resolve rumours can have harmful real-world consequences. |
| Approach: | They propose a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. |
| Outcome: | The proposed methods filter out erroneous model predictions and prioritise them for a human fact-checker. |
Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification (2021.findings-acl)
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| Challenge: | Existing approaches to rumour veracity classification relied on feature engineering. |
| Approach: | They propose a model which disentangles the informational content of a tweet from the manner in which it is written. |
| Outcome: | The proposed model disentangles the informational content of a tweet from the manner in which the information is written. |
PANACEA: An Automated Misinformation Detection System on COVID-19 (2023.eacl-demo)
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Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He
| Challenge: | Using social media and fact-checking to detect misinformation is not enough to prevent the spread of false information. |
| Approach: | They propose a web-based misinformation detection system PANACEA which has two modules, fact-checking and rumour detection. |
| Outcome: | The system outperforms state-of-the-art methods and adapts graph convolutional networks model to detect rumours based on tweets rather than knowledge bases. |
Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling (2024.eacl-demo)
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Talia Tseriotou, Ryan Chan, Adam Tsakalidis, Iman Munire Bilal, Elena Kochkina, Terry Lyons, Maria Liakata
| Challenge: | Existing work on temporal and longitudinal language modelling has focused on taskoriented models. |
| Approach: | They propose an open-source, pip installable toolkit that incorporates Signature-based Neural Network models into various longitudinal language modelling tasks. |
| Outcome: | The proposed model outperforms Transformer-based models in three NLP tasks and provides guidance for future projects. |