Zac Yu
Zac Yu joined Google in 2019 and works with other researchers on machine learning applications to develop deep understanding of objects and entities (e.g., applying large language models to extract product attribute values). He received a BPhil degree in computer science and mathematics from the University of Pittsburgh, and an MS degree in computer science from the University of Texas at Austin. Prior to joining Google, Zac worked in human-computer interaction, learning technology, and computer vision. His thesis work was on the combination of image synthesis and interactive search for search efficiency improvement.
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MAVE: A Product Dataset for Multi-source Attribute Value Extraction
Qifan Wang
Anand Kulkarni
Bin Shu
Jon Elsas
WSDM 2022 (2022)
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Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval and recommendations. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications.
In this paper, we introduce MAVE, a new dataset to better facilitate research on product attribute value extraction. MAVE is composed of a curated set of 2.2 million products from Amazon pages, with 3 million attribute-value annotations across 1257 unique categories. MAVE has four main and unique advantages: First, MAVE is the largest product attribute value extraction dataset by the number of attribute-value examples by 8x. Second, MAVE includes multi-source representations from the product, which captures the full product information with high attribute coverage. Third, MAVE represents a more diverse set of attributes and values relative to what previous datasets cover.
Lastly, MAVE provides a very challenging zero-shot test set, as we empirically illustrate in the experiments. We further propose a novel approach that effectively extracts the attribute value from the multi-source product information. We conduct extensive experiments with several baselines and show that MAVE is challenging for the latest attribute value extraction models, especially on zero-shot attribute extraction.
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Syntharch: Interactive Image Search With Attribute-Conditioned Synthesis
Adriana Kovashka
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020), pp. 645-654
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The use of interactive systems has been found to be a promising approach for content-based image retrieval, the task of retrieving a specific image from a database based on its content. These systems allow the user to refine the set of results iteratively until the target is reached. In order to proceed with the search efficiently, conventional methods rely on some shared knowledge between the user and the system, such as semantic visual attributes of the images. Those approaches demand the images to be semantically labeled and introduce a semantic gap between the two parties' understanding. In this paper, we explore an alternative approach to interactive image search where feedback is elicited exclusively in visual forms, therefore eliminating the semantic gap and allowing for a generalized version of the method to operate on unlabeled databases. We present Syntharch, a novel interactive image search approach which uses synthesized images as options for feedback, instead of asking textual questions to gain information on the relative attribute values of the target image. We further demonstrate that by using synthesized images rather than real images retrieved from the database as feedback options, Syntharch causes less confusion to the user. Finally, we establish that our proposed search method performs similarly or better in comparison to the conventional approach.
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Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach
Qifan Wang
D. Sivakumar
Bin Shu
Jon Elsas
SIGKDD 2020 (2020)
Preview abstract
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information.
It is an important research topic which has been widely studied in e-Commerce and relation learning.
There are two main limitations in existing attribute value extraction methods: scalability and generalizability.
Most existing methods treat each attribute independently and build separate models for each of them, which are not suitable for large scale attribute systems in real-world applications.
Moreover, very limited research has focused on generalizing extraction to new attributes.
In this work, we propose a novel approach for Attribute Value Extraction via Question Answering (AVEQA) using a multi-task framework.
In particular, we build a question answering model which treats each attribute as a question and identifies the answer span corresponding to the attribute value in the product context.
A unique BERT contextual encoder is adopted and shared across all attributes to encode both the context and the question, which makes the model scalable.
A distilled masked language model with knowledge distillation loss is introduced to improve the model generalization ability. In addition, we employ a no-answer classifier to explicitly handle the cases where there are no values for a given attribute in the product context.
The question answering, distilled masked language model and the no answer classification are then combined into a unified multi-task framework.
We conduct extensive experiments on a public dataset. The results demonstrate that the proposed approach outperforms several state-of-the-art methods with large margin.
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