  In Natural Language Processing (NLP), predicting linguistic structures, such
as parsing and chunking, has mostly relied on manual annotations of syntactic
structures. This paper introduces an unsupervised approach to chunking, a
syntactic task that involves grouping words in a non-hierarchical manner. We
present a two-layer Hierarchical Recurrent Neural Network (HRNN) designed to
model word-to-chunk and chunk-to-sentence compositions. Our approach involves a
two-stage training process: pretraining with an unsupervised parser and
finetuning on downstream NLP tasks. Experiments on the CoNLL-2000 dataset
reveal a notable improvement over existing unsupervised methods, enhancing
phrase F1 score by up to 6 percentage points. Further, finetuning with
downstream tasks results in an additional performance improvement.
Interestingly, we observe that the emergence of the chunking structure is
transient during the neural model's downstream-task training. This study
contributes to the advancement of unsupervised syntactic structure discovery
and opens avenues for further research in linguistic theory.
