Doctoral Candidate at QMUL


Rrubaa Panchendrarajan

DC 11 at QMUL

Bringing a wealth of experience in NLP, Rrubaa is presently engaged in an exciting project focused on cross-lingual claim detection.

As a passionate Natural Language Processing (NLP) researcher with over 7 years of experience, Rrubaa consistently seeks to address real-world NLP challenges. She attained her Bachelor’s in Computer Science and Engineering from the University of Moratuwa. Subsequently, she completed her Master’s in Computer Science at the National University of Singapore, an institution consistently ranked among the world’s top universities.

Throughout her professional trajectory, Rrubaa has worn various hats, including roles such as Software Engineer, Lecturer, and Research Associate in both industry and academia. This diverse experience has equipped her with a unique ability to bridge the gap between the academic realm and industry practices, allowing her to become a well-rounded professional.

Beyond her professional pursuits, Rrubaa is a cherished wife, daughter, and sister, loving to spend quality time with her family.

DC11 Individual Project

Cross-lingual claim detection for fact-checking

Research Objectives:

Strategic Claim Detection: Paving the Way for Fact-Checking

To design a strategy to detect potentially verifiable sentences, also known as claims; this is the first task before producing an informed assessment of the veracity of the claim, known as fact-checking.

Annotation Criteria for Truth Claims: Building a Multilingual Political Dataset

To define annotation criteria for claim detection and collect a multilingual dataset of claims annotated with those criteria within the political domain.

Multilingual Data Handling: Bridging Linguistic Divides in Political Discourse

Use cross-lingual methods to deal with multilingual data

Clustered Claims and Contradictions: Enhancing Fact-Checking and Popularity Assessment

Cluster together related claims, including potential contradictions, which further informs the fact-checking process and helps quantify the popularity of a claim.

Expected Results:

Cross-Lingual Claim Detection and Clustering: Empowering Fact-Checking

Development of a cross-lingual system aimed at detecting and clustering multilingual claims, as a first step both to assist fact-checkers in the selection process and to feed an automated fact-checking system.

Multilingual Dataset of Political Claims: A Valuable Resource for Fact-Checking

Elaboration of a multilingual dataset of political claims labelled according to the criteria considered in the previous analysis.

Fact-Checking in Action: Use Cases on Climate Emergency and Health

Use cases on climate emergency and health.


In our pursuit of academic excellence, HYBRIDS Doctoral Candidates are guided by a dedicated team of supervisors. Comprising the Main Supervisor, Co-Supervisor, and Inter-sectoral Supervisor, this team of professionals offers a wealth of knowledge, mentorship, and interdisciplinary insights.

Main Supervisor

Dr. Arkaitz Zubiaga

Queen Mary University of London (QMUL)


Dr. Rubén Miguez

Newtral Media Audiovisual (NEWTRAL)

Inter-sectoral Supervisors

Ms. Stephanie Öttl

Industrieanlagen- Betriebsgesellschaft (IABG)

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