A Unified Generative Approach to Product Attribute-Value Identification (2023)

Abstract: 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.

## introduction since organized product data play a crucial role in serving better product search and recommendation to customers, product attribute value identification (pavi) has been a core task in the e-commerce industry. for attributes pre-defined by e-commerce sites, the task aims to link values of those attributes to products using product titles and descriptions as clues (figure 1 ). for example, from the title "d&g cotton piqué polo shirt designed and manufactured in italy," models are required to return a set of possible attribute-value pairs, namely {⟨brand, dolce & gabbana⟩, ⟨material, cotton⟩, ⟨country of origin, italy⟩, ⟨country of design, italy⟩}. in the literature, pavi has been addressed basically by extraction from the product text by using named entity recognition @xcite ; it takes product text to return a set of attribute-value pairs. in this example, the model generates dolce & gabbana as a brand, which is a canonicalized form of d&g, and two attributes have the entity italy as values. @xcite @xcite @xcite @xcite @xcite or question answering @xcite @xcite . however, since pavi requires canonicalized values rather than raw value strings in the product text, some researchers have started to solve pavi as classification @xcite . to adopt pavi models in real-world e-commerce platforms, there are the following challenges. unseen values. since values can be entities such as brands, models need to identify values unseen in the training data @xcite . since the classification-based approach assumes a predefined set of target classes (attribute-value pairs), it cannot handle such unseen attribute-value pairs. multi-attribute values. when values can be associated with multiple attributes (e.g., italy in figure 1 ), models need to identify multiple attributes for a single value string in the text. to address this, the extraction-based approach must solve nested named entity recognition @xcite . ## proposed method as mentioned above, previous studies formalize pavi as either sequence tagging or multi-label classification problems. these approaches do not address all the challenges derived from real-world e-commerce sites at the same time ( we thus propose a unified generative framework that formalizes pavi as a sequence-to-set problem. let us denote @xmath0, x 2 , . . . , x n } as product data (title and description) where n is the number of tokens in x. given product data x, the model is trained to return a set of attribute-value pairs @xmath1, v 1 ⟩, ⟨a 2 , v 2 ⟩, . . . , ⟨a k , v k ⟩} for x, where k is the number of attribute-value pairs associated with the product; a @xmath2, a 2 , . . . , a m i } and v @xmath3, v 2 , . . . , v l i } are corresponding attribute and value. 1 m i and l i are the numbers of tokens in a i and v i , respectively. as the backbone of our approach, we employ t5 @xcite , a pre-trained generative 1 when there are more than one value for the same attribute (e.g., size), we decompose a pair of attribute and n(> 1) multiple values into n attribute-value pairs ( the key issue in formulating the pavi task as sequence-to-sequence generation is how to linearize a set of attribute-value pairs into a sequence. firstly, we should consider how to associate attributes and their corresponding values in the output sequence. secondly, the autoregressive generation decodes output tokens (here, attributes and values) one by one conditioned on the previous labels. thus, if specific (or informative) tokens are first decoded, it will make it easy to decode the remaining tokens. however, due to the exposure bias, decoding specific (namely, infrequent) tokens are more likely to fail. to address the challenge, we decompose the issue on linearization into two subproblems on how to compose an attribute-value pair and how to order attribute-value pairs. in what follows, we will describe these subproblems. ## experiments we evaluate our generative approach to pavi using two real-world datasets. in the literature, different types of approaches are rarely compared due to the proprietary nature of codes and datasets in this task. we thus compare our generation-based model with extraction-and classification-based models, all of which are based on public pre-trained models, using not only in-house but also public datasets.