Natural language is hierarchically structured: smaller units (eg, phrases) are nested within
larger units (eg, clauses). When a larger constituent ends, all of the smaller constituents that …

Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep
learning community for some time. However, the difficulty of training memory models …

While standard recurrent neural networks explicitly impose a chain structure on different
forms of data, they do not have an explicit bias towards recursive self-instantiation where the …

Various models have been proposed to incorporate knowledge of syntactic structures into
neural language models. However, previous works have relied heavily on elaborate …

Recently, neural network models which automatically infer syntactic structure from raw text
have started to achieve promising results. However, earlier work on unsupervised parsing …

Since their inception, encoder-decoder models have successfully been applied to a wide
array of problems in computational linguistics. The most recent successes are predominantly …

We model the recursive production property of context-free grammars for natural and
synthetic languages. To this end, we present a dynamic programming algorithm that …

Inducing latent tree structures from sequential data is an emerging trend in the NLP research
landscape today, largely popularized by recent methods such as Gumbel LSTM and …

In this thesis, we try to build a connection between the two schools by introducing syntactic
inductive biases for deep learning models. We propose two families of inductive biases, one …

Abstract We investigate Recursive Neural Networks (RvNNs) for language processing tasks.
Roughly, from a generalized perspective, RvNNs repeatedly apply some neural function on …