The paper hypothesizes that next-byte and boundary distributions in byte-level LMs can be disentangled, proposes two experiments to test it, but provides no experimental results.
MAUPQA: Massive Automatically-created Polish Question Answering Dataset
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.
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cs.CL 1years
2026 1verdicts
REJECT 1representative citing papers
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Disentangling Language Modeling and Boundaries
The paper hypothesizes that next-byte and boundary distributions in byte-level LMs can be disentangled, proposes two experiments to test it, but provides no experimental results.