  To model the dependencies between utterances in multi-party conversations, we
propose a simple and generic framework based on the dependency parsing results
of utterances. Particularly, we present an approach to encoding the
dependencies in the form of relative dependency encoding (ReDE) and illustrate
how to implement it in Transformers by modifying the computation of
self-attention. Experimental results on four multi-party conversation
benchmarks show that this framework successfully boosts the general performance
of two Transformer-based language models and leads to comparable or even
superior performance compared to the state-of-the-art methods. The codes are
available at https://github.com/shenwzh3/ReDE.

Computation and Language