  Contracts are a common type of legal document that frequent in several
day-to-day business workflows. However, there has been very limited NLP
research in processing such documents, and even lesser in generating them.
These contracts are made up of clauses, and the unique nature of these clauses
calls for specific methods to understand and generate such documents. In this
paper, we introduce the task of clause recommendation, asa first step to aid
and accelerate the author-ing of contract documents. We propose a two-staged
pipeline to first predict if a specific clause type is relevant to be added in
a contract, and then recommend the top clauses for the given type based on the
contract context. We pretrain BERT on an existing library of clauses with two
additional tasks and use it for our prediction and recommendation. We
experiment with classification methods and similarity-based heuristics for
clause relevance prediction, and generation-based methods for clause
recommendation, and evaluate the results from various methods on several clause
types. We provide analyses on the results, and further outline the advantages
and limitations of the various methods for this line of research.
