Papers by Pavel Smrz

3 papers
Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction (2023.findings-acl)

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Challenge: a novel latent variable model for fact-checking and analysis learns to identify the veracity of a claim and its relevant evidences.
Approach: They propose to disentangle the per-evidence relevance probability and its contribution to the final veracity probability in an interpretable way.
Outcome: The proposed model can achieve competitive results on the FEVER dataset while using significantly fewer parameters.
BelarusianGLUE: Towards a Natural Language Understanding Benchmark for Belarusian (2025.acl-long)

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Challenge: Recent advances in NLP, such as large language models, have had groundbreaking impact on the field.
Approach: They propose a benchmark for Belarusian, an East Slavic language, with 15K instances in five tasks: sentiment analysis, linguistic acceptability, word in context, Winograd schema challenge, textual entailment.
Outcome: The proposed model underperforms on sentiment analysis, linguistic acceptability, word in context, Winograd schema challenge and textual entailment, but is competitive for linguistic acceptance.
R2-D2: A Modular Baseline for Open-Domain Question Answering (2021.findings-emnlp)

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Challenge: Using extractive and generative reader, we demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA.
Approach: They propose a four-stage open-domain QA pipeline with a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system’s components.
Outcome: The proposed pipeline outperforms state-of-the-art on three open-domain QA datasets and is twice as effective as the posterior averaging ensemble of the same models with different parameters.

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