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Retail item data contains many different forms of text like the title of anitem, the description of an item, item name and reviews. It is of interest toidentify 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 formedsentence while the description is made up of well formed sentences. In thiswork, we use a triplet loss to contrast the embeddings of the item title withthe description to establish a proof of concept. We find that using the tripletloss in a multi-task NER algorithm improves both the precision and recall by asmall percentage. While the improvement is small, we think it is a step in theright direction of using various forms of text in a multi-task algorithm. Inaddition to precision and recall, the multi task triplet loss method is alsofound to significantly improve the exact match accuracy i.e. the accuracy oftagging the entire set of tokens in the text with correct tags.

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