Creating a silver standard for patent simplification
This work addresses the challenge of making patent documents more accessible for humans and machines in information retrieval, though it is incremental as it builds on existing paraphrasing methods with added filters.
The paper tackled the problem of simplifying complex patent text by automatically generating a large-scale silver standard dataset, as no in-domain parallel data existed. The result was a cleaner corpus that human evaluation found grammatical, adequate, and containing simple sentences, enabling successful training of a simplification system.
Patents are legal documents that aim at protecting inventions on the one hand and at making technical knowledge circulate on the other. Their complex style -- a mix of legal, technical, and extremely vague language -- makes their content hard to access for humans and machines and poses substantial challenges to the information retrieval community. This paper proposes an approach to automatically simplify patent text through rephrasing. Since no in-domain parallel simplification data exist, we propose a method to automatically generate a large-scale silver standard for patent sentences. To obtain candidates, we use a general-domain paraphrasing system; however, the process is error-prone and difficult to control. Thus, we pair it with proper filters and construct a cleaner corpus that can successfully be used to train a simplification system. Human evaluation of the synthetic silver corpus shows that it is considered grammatical, adequate, and contains simple sentences.