  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 to be solved by 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 problem 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 the entity typing task 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
CharacterBERT embedding with a two-layer MLP classifier 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.
