Ying Xie


2024

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Assessing Image-Captioning Models: A Novel Framework Integrating Statistical Analysis and Metric Patterns
Qiaomu Li | Ying Xie | Nina Grundlingh | Varsha Rani Chawan | Cody Wang
Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024

In this study, we present a novel evaluation framework for image-captioning models that integrate statistical analysis with common evaluation metrics, utilizing two popular datasets, FashionGen and Amazon, with contrasting dataset variation to evaluate four models: Video-LLaVa, BLIP, CoCa and ViT-GPT2. Our approach not only reveals the comparative strengths of models, offering insights into their adaptability and applicability in real-world scenarios but also contributes to the field by providing a comprehensive evaluation method that considers both statistical significance and practical relevance to guide the selection of models for specific applications. Specifically, we propose Rank Score as a new evaluation metric that is designed for e-commerce image search applications and employ CLIP Score to quantify dataset variation to offer a holistic view of model performance.

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Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results
Duleep Rathgamage Don | Ying Xie | Le Yu | Simon Hughes | Yun Zhu
Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024

This paper proposes a novel method to improve the accuracy of product search in e-commerce by utilizing a cluster language model. The method aims to address the limitations of the bi-encoder architecture while maintaining a minimal additional training burden. The approach involves labeling top products for each query, generating semantically similar query clusters using the K-Means clustering algorithm, and fine-tuning a global language model into cluster language models on individual clusters. The parameters of each cluster language model are fine-tuned to learn local manifolds in the feature space efficiently, capturing the nuances of various query types within each cluster. The inference is performed by assigning a new query to its respective cluster and utilizing the corresponding cluster language model for retrieval. The proposed method results in more accurate and personalized retrieval results, offering a superior alternative to the popular bi-encoder based retrieval models in semantic search.