  In this paper, we propose a sequential neural encoder with latent structured
description (SNELSD) for modeling sentences. This model introduces latent
chunk-level representations into conventional sequential neural encoders, i.e.,
recurrent neural networks (RNNs) with long short-term memory (LSTM) units, to
consider the compositionality of languages in semantic modeling. An SNELSD
model has a hierarchical structure that includes a detection layer and a
description layer. The detection layer predicts the boundaries of latent word
chunks in an input sentence and derives a chunk-level vector for each word. The
description layer utilizes modified LSTM units to process these chunk-level
vectors in a recurrent manner and produces sequential encoding outputs. These
output vectors are further concatenated with word vectors or the outputs of a
chain LSTM encoder to obtain the final sentence representation. All the model
parameters are learned in an end-to-end manner without a dependency on
additional text chunking or syntax parsing. A natural language inference (NLI)
task and a sentiment analysis (SA) task are adopted to evaluate the performance
of our proposed model. The experimental results demonstrate the effectiveness
of the proposed SNELSD model on exploring task-dependent chunking patterns
during the semantic modeling of sentences. Furthermore, the proposed method
achieves better performance than conventional chain LSTMs and tree-structured
LSTMs on both tasks.
