Multi-Task Triplet Loss for Named Entity Recognition using Supplementary Text

TL;DR

This paper introduces a multi-task NER approach that employs triplet loss to leverage supplementary text forms, such as item descriptions, improving overall tagging accuracy in retail item data.

Contribution

It presents a novel use of triplet loss within a multi-task NER framework to enhance entity recognition across different text formats.

Findings

Small improvements in precision and recall.

Significant increase in exact match accuracy.

Proof of concept for using triplet loss in multi-form text NER.

Abstract

Retail item data contains many different forms of text like the title of an item, the description of an item, item name and reviews. It is of interest to identify the item name in the other forms of text using a named entity tagger. However, the title of an item and its description are syntactically different (but semantically similar) in that the title is not necessarily a well formed sentence while the description is made up of well formed sentences. In this work, we use a triplet loss to contrast the embeddings of the item title with the description to establish a proof of concept. We find that using the triplet loss in a multi-task NER algorithm improves both the precision and recall by a small percentage. While the improvement is small, we think it is a step in the right direction of using various forms of text in a multi-task algorithm. In addition to precision and recall, the multi task triplet loss method is also found to significantly improve the exact match accuracy i.e. the accuracy of tagging the entire set of tokens in the text with correct tags.