  Inferring implicit discourse relations in natural language text is the most
difficult subtask in discourse parsing. Surface features achieve good
performance, but they are not readily applicable to other languages without
semantic lexicons. Previous neural models require parses, surface features, or
a small label set to work well. Here, we propose neural network models that are
based on feedforward and long-short term memory architecture without any
surface features. To our surprise, our best configured feedforward architecture
outperforms LSTM-based model in most cases despite thorough tuning. Under
various fine-grained label sets and a cross-linguistic setting, our feedforward
models perform consistently better or at least just as well as systems that
require hand-crafted surface features. Our models present the first neural
Chinese discourse parser in the style of Chinese Discourse Treebank, showing
that our results hold cross-linguistically.
