We present an incremental syntactic representation that consists of assigning a single discrete label to each word in a sentence, where the label is predicted using strictly incremental processing of a prefix of the sentence, and the sequence of labels for a sentence fully determines a parse tree...

This paper introduces a novel generation system that composes humanlike descrip- tions of images from computer vision de- tections. By leveraging syntactically in- formed word co-occurrence statistics, the generator filters and constrains the noisy detections output from a vision system to generate...

Model interpretability methods are often used to explain NLP model decisions on tasks such as text classification, where the output space is relatively small. However, when applied to language generation, where the output space often consists of tens of thousands of tokens, these methods are unable...