RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts
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.
67f78485cfcd3569910c99ab
Natalia Loukachevitch
67f78485cfcd3569910c99ac
Natalia Tkachenko
67f78485cfcd3569910c99ad
Anna Lapanitsyna
67f78485cfcd3569910c99ae
Mikhail Tikhomirov
extracted_confirmed
2025-04-10T09:18:43.707Z
652cedbdf120598322ae358a
https://cdn-avatars.huggingface.co/v1/production/uploads/652cedbdf120598322ae358a/RrxrP0gtQus4SfNwfyAg_.jpeg
Mikhail
user
RefalMachine
67f78485cfcd3569910c99af
Nicolay Rusnachenko
claimed_verified
2025-04-10T09:57:54.353Z
64e62d11d27a8292c3637f86
https://cdn-avatars.huggingface.co/v1/production/uploads/64e62d11d27a8292c3637f86/aptDeBHpCJxcREj6KPLN1.jpeg
Nicolay Rusnachenko
user
nicolay-r
RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts
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.
The paper describes a Dialogue Evaluation shared task for extracting structured opinions from Russian news texts using large language models in various settings, with fine-tuning delivering the best results.
large language models zero-shot few-shot fine-tuning opinion tuples sentiment holder target expression sentiment prompts