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Found 10 papers, 2 papers with code
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction
A major problem in solving AVE as XMC is that the distribution between positive and negative labels for products is heavily imbalanced.
RakutenAI-7B: Extending Large Language Models for Japanese
We introduce RakutenAI-7B, a suite of Japanese-oriented large language models that achieve the best performance on the Japanese LM Harness benchmarks among the open 7B models.
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
In this paper, we reformulate this task as a multi-label classification task that can be applied for real-world scenario in which only annotation of attribute values is available to train models (i. e., annotation of positional information of attribute values is not available).
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction
Existing attribute-value extraction (AVE) models require large quantities of labeled data for training.
A Unified Generative Approach to Product Attribute-Value Identification
We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text.
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction
A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products.
Unsupervised AMR-Dependency Parse Alignment
In this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner.
