Many problems in Natural Language Processing (NLP) involves an
efficient search for the best derivation over (exponentially) many
candidates, especially in parsing and machine translation. In these
cases, the
concept of "packed forest" provides a compact representation of the
huge search spaces, where efficient inference algorithms based on
Dynamic Programming (DP) are possible.
In this talk we address two important open problems within this
framework: exact k-best inference which is often used in NLP
pipelines such as parse reranking and MT rescoring, and approximate
inference when the search space is too big for exact search.
No comments:
Post a Comment