  The task of semantic role labeling (SRL) is dedicated to finding the
predicate-argument structure. Previous works on SRL are mostly supervised and
do not consider the difficulty in labeling each example which can be very
expensive and time-consuming. In this paper, we present the first neural
unsupervised model for SRL. To decompose the task as two argument related
subtasks, identification and clustering, we propose a pipeline that
correspondingly consists of two neural modules. First, we train a neural model
on two syntax-aware statistically developed rules. The neural model gets the
relevance signal for each token in a sentence, to feed into a BiLSTM, and then
an adversarial layer for noise-adding and classifying simultaneously, thus
enabling the model to learn the semantic structure of a sentence. Then we
propose another neural model for argument role clustering, which is done
through clustering the learned argument embeddings biased towards their
dependency relations. Experiments on CoNLL-2009 English dataset demonstrate
that our model outperforms previous state-of-the-art baseline in terms of
non-neural models for argument identification and classification.
