Papers by Keiji Shinzato
Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? (2023.emnlp-industry)
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| Challenge: | scalability of attribute-value extraction (AVE) task is key for a large number of products . a question-answering (QA)-based approach is better for AVE, but requires a larger number of classes to be scalable. |
| Approach: | They propose a question-answering-based approach that additionally inputs the target attribute as a query to extract its values. |
| Outcome: | The proposed approach outperforms a classical approach on real-word e-commerce datasets in accuracy and speed. |
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction (2022.acl-short)
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| Challenge: | Existing approaches to extract value from product data for a large number of attributes are not effective for rare and ambiguous attributes. |
| Approach: | They propose to use attributes as knowledge to expand AVE queries by retrieving possible answers from training data. |
| Outcome: | The proposed model improves on a cleaned version of AliExpress dataset for rare and ambiguous attributes, especially for rare attributes. |
A Unified Generative Approach to Product Attribute-Value Identification (2023.findings-acl)
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| Challenge: | Product attribute value identification (PAVI) is a core task in the e-commerce industry. |
| Approach: | They propose a generative approach to product attribute-value identification (PAVI) they use product text to decode a set of attribute- value pairs as a target sequence from the given product text. |
| Outcome: | The proposed approach outperforms extraction- and classification-based methods on large-scale real-world datasets. |
Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching (2022.emnlp-industry)
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| Challenge: | Existing approaches to match seller listed items to appropriate product are computationally heavy and require computational resources. |
| Approach: | They propose a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoding model. |
| Outcome: | The proposed approach improves 4% absolute accuracy when no training data is available and 2% when annotated training data exists. |