Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing

The explosion of e-commerce has caused the need for processing and analysis of product titles, like entity typing in product titles. However, the rapid activity in e-commerce has led to the rapid emergence of new entities, which is difficult for general entity typing. Besides, product titles in e-commerce have very different language styles from text data in general domain. In order to handle new entities in product titles and address the special language styles of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing. First, we reformulate entity typing into a textual entailment problem to handle new entities that are not present during training. Second, we design a model to automatically generate textual entailment hypotheses using a continuous prompt tuning method, which can generate better textual entailment hypotheses without manual design. Third, we utilize the fusion embeddings of BERT embedding and Char-acterBERT embedding to solve the problem that the language styles of product titles in e-commerce are different from that of general domain. To analyze the effect of each contribution, we compare the performance of entity typing and textual entailment model, and conduct ablation studies on continuous prompt tuning and fusion embeddings. We also evaluate the impact of different prompt template initialization for the continuous prompt tuning. We show our proposed model improves the average F1 score by around 2% compared to the baseline BERT entity typing model.

I. Introduction

Nowadays, the boom of e-commerce has led to an increasing preference for online shopping. In order to better manage products and provide services to customers, such as classifying products and recommending products, e-commerce platforms need to understand entities in the product titles, such as brand names (Apple, Nike, etc.), product names (iPhone, shoes, etc.) and other features (colors, sizes, etc.). This task is usually formulated as entity typing, which is to classify entity types given the entity and context. For example, a product title ‘NAGANO Set of 2 Chairs’ in Table I has ‘NAGANO’ as a brand name and ‘Chairs’ as a product name. Thus the entity typing task is to classify ‘NAGANO’ and ‘Chairs’ into ‘brand name’, ‘product name’ or ‘feature’ based on the product title.