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Found 6 papers, 1 papers with code
Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis
In the era of rapid evolution of generative language models within the realm of natural language processing, there is an imperative call to revisit and reformulate evaluation methodologies, especially in the domain of aspect-based sentiment analysis (ABSA).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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HistRED: A Historical Document-Level Relation Extraction Dataset
To demonstrate the usefulness of our dataset, we propose a bilingual RE model that leverages both Korean and Hanja contexts to predict relations between entities.
Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime.
CG-NeRF: Conditional Generative Neural Radiance Fields
While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics.
Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation
Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts.
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.
