16 citations · 40 across the 12 of their papers we have counts for
13 papers · 1 filter
Generative Models Enhanced by Sequence Labelling and Aspect-Code Switching Improve Cross-lingual Aspect-Based Sentiment Analysis
Jakub Šmíd, Pavel Přibáň, Pavel Král
Cross-lingual aspect-based sentiment analysis (ABSA) transfers knowledge from a source language with annotated data to a target language, enabling fine-grained sentiment analysis w…
Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks
Jakub Šmíd, Pavel Přibáň, Pavel Král
This paper introduces a novel Czech dataset in the restaurant domain for aspect-based sentiment analysis (ABSA), enriched with annotations of opinion terms. The dataset supports th…
Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
Jakub Šmíd, Pavel Přibáň, Pavel Král
While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current c…
Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models
Jakub Šmíd, Pavel Přibáň, Pavel Král
Aspect-based sentiment analysis (ABSA) has made significant strides, yet challenges remain for low-resource languages due to the predominant focus on English. Current cross-lingual…
LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation
Jakub Šmíd, Pavel Přibáň, Pavel Král
Cross-lingual aspect-based sentiment analysis (ABSA) involves detailed sentiment analysis in a target language by transferring knowledge from a source language with available annot…
Prompt-Based Approach for Czech Sentiment Analysis
Jakub Šmíd, Pavel Přibáň
This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve t…