Wei-Te Chen


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Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction?
Wei-Te Chen | Keiji Shinzato | Naoki Yoshinaga | Yandi Xia
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

The key challenge in the attribute-value extraction (AVE) task from e-commerce sites is the scalability to diverse attributes for a large number of products in real-world e-commerce sites. To make AVE scalable to diverse attributes, recent researchers adopted a question-answering (QA)-based approach that additionally inputs the target attribute as a query to extract its values, and confirmed its advantage over a classical approach based on named-entity recognition (NER) on real-word e-commerce datasets. In this study, we argue the scalability of the NER-based approach compared to the QA-based approach, since researchers have compared BERT-based QA-based models to only a weak BiLSTM-based NER baseline trained from scratch in terms of only accuracy on datasets designed to evaluate the QA-based approach. Experimental results using a publicly available real-word dataset revealed that, under a fair setting, BERT-based NER models rival BERT-based QA models in terms of the accuracy, and their inference is faster than the QA model that processes the same product text several times to handle multiple target attributes.

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A Unified Generative Approach to Product Attribute-Value Identification
Keiji Shinzato | Naoki Yoshinaga | Yandi Xia | Wei-Te Chen
Findings of the Association for Computational Linguistics: ACL 2023

Product attribute-value identification (PAVI) has been studied to link products on e-commerce sites with their attribute values (e.g., ⟨Material, Cotton⟩) using product text as clues. Technical demands from real-world e-commerce platforms require PAVI methods to handle unseen values, multi-attribute values, and canonicalized values, which are only partly addressed in existing extraction- and classification-based approaches. Motivated by this, we explore a generative approach to the PAVI task. 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. Since the attribute value pairs are unordered set elements, how to linearize them will matter; we, thus, explore methods of composing an attribute-value pair and ordering the pairs for the task. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods on large-scale real-world datasets meant for those methods.


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Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction
Wei-Te Chen | Yandi Xia | Keiji Shinzato
Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

Although most studies have treated attribute value extraction (AVE) as named entity recognition, these approaches are not practical in real-world e-commerce platforms because they perform poorly, and require canonicalization of extracted values. Furthermore, since values needed for actual services is static in many attributes, extraction of new values is not always necessary. Given the above, we formalize AVE as extreme multi-label classification (XMC). A major problem in solving AVE as XMC is that the distribution between positive and negative labels for products is heavily imbalanced. To mitigate the negative impact derived from such biased distribution, we propose label masking, a simple and effective method to reduce the number of negative labels in training. We exploit attribute taxonomy designed for e-commerce platforms to determine which labels are negative for products. Experimental results using a dataset collected from a Japanese e-commerce platform demonstrate that the label masking improves micro and macro F1 scores by 3.38 and 23.20 points, respectively.

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Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction
Keiji Shinzato | Naoki Yoshinaga | Yandi Xia | Wei-Te Chen
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products. Although this challenge is partially addressed by a question answering (QA) approach which finds a value in product data for a given query (attribute), it does not work effectively for rare and ambiguous queries. We thus propose simple knowledge-driven query expansion based on possible answers (values) of a query (attribute) for QA-based AVE. We retrieve values of a query (attribute) from the training data to expand the query. We train a model with two tricks, knowledge dropout and knowledge token mixing, which mimic the imperfection of the value knowledge in testing. Experimental results on our cleaned version of AliExpress dataset show that our method improves the performance of AVE (+6.08 macro F1), especially for rare and ambiguous attributes (+7.82 and +6.86 macro F1, respectively).


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Unsupervised AMR-Dependency Parse Alignment
Wei-Te Chen | Martha Palmer
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. Our aligner involves several different features, including named entity tags and semantic role labels, and uses Expectation-Maximization training. Results show that our aligner reaches an 87.1% F-Score score with the experimental data, and enhances AMR parsing.


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SemEval-2016 Task 12: Clinical TempEval
Steven Bethard | Guergana Savova | Wei-Te Chen | Leon Derczynski | James Pustejovsky | Marc Verhagen
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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Learning to Map Dependency Parses to Abstract Meaning Representations
Wei-Te Chen
Proceedings of the ACL-IJCNLP 2015 Student Research Workshop


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Anafora: A Web-based General Purpose Annotation Tool
Wei-Te Chen | Will Styler
Proceedings of the 2013 NAACL HLT Demonstration Session


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E-HowNet and Automatic Construction of a Lexical Ontology
Wei-Te Chen | Su-Chu Lin | Shu-Ling Huang | You-Shan Chung | Keh-Jiann Chen
Coling 2010: Demonstrations