Recent work on word ordering has argued that syntactic structure is important, or even
required, for effectively recovering the order of a sentence. We find that, in fact, an n-gram …

As neural language models approach human performance on NLP benchmark tasks, their
advances are widely seen as evidence of an increasingly complex understanding of syntax …

Sequential information, aka, orders, is assumed to be essential for processing a sequence
with recurrent neural network or convolutional neural network based encoders. However, is …

It has been shown that transition-based methods can be used for syntactic word ordering
and tree linearization, achieving significantly faster speed compared with traditional best-first …

Word ordering is a constrained language generation task taking unordered words as input.
Existing work uses linear models and neural networks for the task, yet pre-trained language …

Lexical analysis and parsing tasks model the deeper properties of the words and their
relationships to each other. The commonly used techniques involve word segmentation, part …

The task of linearization is to find a grammatical order given a set of words. Traditional
models use statistical methods. Syntactic linearization systems, which generate a sentence …

Traditional methods for deep NLG adopt pipeline approaches comprising stages such as
constructing syntactic input, predicting function words, linearizing the syntactic input and …

Transition-based techniques were originally introduced for syntactic parsing. They have
achieved the highest accuracies for both constituency and dependency parsing. In an earlier …