A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing
Yoav Tulpan Oren Tsur
Online social platforms provide a bustling arena for information-sharing and
for multi-party discussions. Various frameworks for dialogic discourse parsing
were developed and used for the processing of discussions and for predicting
the productivity of a dialogue. However, most of these frameworks are not
suitable for the analysis of contentious discussions that are commonplace in
many online platforms. A novel multi-label scheme for contentious dialog
parsing was recently introduced by Zakharov et al. (2021). While the schema is
well developed, the computational approach they provide is both naive and
inefficient, as a different model (architecture) using a different
representation of the input, is trained for each of the 31 tags in the
annotation scheme. Moreover, all their models assume full knowledge of label
collocations and context, which is unlikely in any realistic setting. In this
work, we present a unified model for Non-Convergent Discourse Parsing that does
not require any additional input other than the previous dialog utterances. We
fine-tuned a RoBERTa backbone, combining embeddings of the utterance, the
context and the labels through GRN layers and an asymmetric loss function.
Overall, our model achieves results comparable with SOTA, without using label
collocation and without training a unique architecture/model for each label.