Papers › RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts
RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts
Natalia Loukachevitch, Natalia Tkachenko, Anna Lapanitsyna, Mikhail Tikhomirov, Nicolay Rusnachenko
In this paper, we introduce the Dialogue Evaluation shared task on extraction of structured opinions from Russian news texts. The task of the contest is to extract opinion tuples for a given sentence; the tuples are composed of a sentiment holder, its target, an expression and sentiment from the holder to the target. In total, the task received more than 100 submissions. The participants experimented mainly with large language models in zero-shot, few-shot and fine-tuning formats. The best result on the test set was obtained with fine-tuning of a large language model. We also compared 30 prompts and 11 open source language models with 3-32 billion parameters in the 1-shot and 10-shot settings and found the best models and prompts.
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