A da T ag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding (2021)

Abstract: Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model’s capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.

## introduction the product profiles on e-commerce platforms are usually comprised of natural texts describing products and their main features. key product features are conveyed in unstructured texts, with limited impact on machine-actionable applications, like search @xcite , recommendation @xcite , and question answering @xcite , among others. automatic attribute value extraction aims to obtain structured product features from product profiles. the input is a textual figure 1 : an example of the product profile on an e-commerce platform. it consists of a title, several information bullets, and a product description. most existing studies on attribute value extraction use neural sequence labeling architectures @xcite @xcite . to handle multiple attributes, one line of previous contributions develops a set of "attribute-specific" models (i.e., one model per attribute). the goal is to construct neural networks with (partially) separate model parameters for different attributes. for example, one can construct an independent sequence labeling model for each attribute and make predictions with all the models collectively (e.g., the vanilla opentag model @xcite ). instead of totally separate models, one can also use different tag sets corresponding to different attributes. these networks can also share the feature encoder and use separate label decoders @xcite . however, the explicit network (component) separation in these contributions constrains knowledge-sharing across different attributes. exposure to other attributes can help in disambiguating the values for each attribute. and having access to the entire training data for all attributes helps with the generic sequence tagging task. another line for multi-attribute extraction contributions learns a single model for all attributes. the model proposed by @xcite , for example, embeds the attribute name with the textual sequence, to achieve a single "attribute-aware" extraction model for all attributes. this approach addresses the issues in the previous direction. however, sharing all the network parameters with all attributes could limit the model's capacity to capture attribute-specific characteristics. in this paper we address the limitations of the existing contribution lines, through adaptive decoder parameterization. we propose to generate a decoder on the fly for each attribute based on its embedding. this results in different but semantically correlated decoders, which maintain the specific characteristics for each attribute, while facilitating knowledge-sharing across different attributes. to this end, we use conditional random fields (crf) @xcite as the decoders, and parameterize the decoding layers with the attribute embedding through a hypernetwork @xcite and a mixture-of-experts (moe) module @xcite . we further explore several pretrained attribute embedding techniques, to add useful attribute-specific external signals. we use both contextualized and static embeddings for the attribute name along with its potential values to capture meaningful semantic representations. we summarize our contributions as follows: ( 1 2 background ## conclusion in this work we propose a multi-attribute value extraction model that performs joint modeling of many attributes using an adaptive crf-based decoder. our model has a high capacity to derive attribute-specific network parameters while facilitating knowledge sharing. incorporated with pretrained attribute embeddings, our model shows marked improvements over previous methods.