Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Human evaluation is viewed as a reliable evaluation method for NLG which is expensive
and time-consuming. In order to save labor and costs, researchers usually perform human …

Abstract Model selection for a given target task can be costly, as it may entail extensive
annotation of the quality of outputs of different models. We introduce DiffUse, an efficient …

Model selection for a given target task can be costly, as it may entail extensive annotation of
the quality of outputs of different models. We introduce DiffUse, an efficient method to make …

A major challenge in the field of Text Generation is evaluation because we lack a sound
theory that can be leveraged to extract guidelines for evaluation campaigns. In this work, we …

Our goal is a mechanism for efficiently assigning scalar ratings to each of a large set of
elements. For example," what percent positive or negative is this product review?" When …

In natural language processing (NLP), many tasks involve structured prediction: predicting
structured outputs consisting of a group of interdependent variables. This allows extracting …