Papers by Andrew Zamai

1 papers
BUSTER: a “BUSiness Transaction Entity Recognition” dataset (2023.emnlp-industry)

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Challenge: Natural Language Processing has seen major breakthroughs in the last few years, but transferring these advances into industry applications can be difficult.
Approach: They propose to use a BUSiness Transaction Entity Recognition dataset to support industry-oriented research by exploiting both general-purpose and domain-specific language models.
Outcome: The proposed model is the best performing model and an additional silver corpus to BUSTER.

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