Learning Cross-Task Attribute - Attribute Similarity for Multi-task Attribute-Value Extraction
Mayank Kumar Jain, Sourangshu Bhattacharya, Harshit Jain, Karimulla Shaik, Muthusamy Chelliah · 2021
Automatic extraction of product attributevalue pairs from unstructured text like product descriptions is an important problem for ecommerce companies.The attribute schema typically varies from one category of products (which will be referred as vertical) to another.This leads to extreme annotation efforts for training of supervised deep sequence labeling models such as LSTM-CRF, and consequently not enough labeled data for some verticalattribute pairs.In this work, we propose a technique for alleviating this problem by using annotated data from related verticals in a multitask learning framework.Our approach relies on availability of similar attributes (labels) in another related vertical.Our model jointly learns the similarity between attributes of the two verticals along with the model parameters for the sequence tagging model.The main advantage of our approach is that it does not need any prior annotation of attribute similarity.Our system has been tested with datasets of size more than 10000 from a large e-commerce company in India.We perform detailed experiments to show that our method indeed increases the macro-F1 scores for attribute value extraction in general, and for labels with low training data in particular.We also report top labels from other verticals that contribute towards learning of particular labels.