Generic Dependency Modeling for Multi-Party Conversation
This addresses the challenge of understanding complex interactions in multi-party conversations for natural language processing applications, representing an incremental improvement.
The authors tackled the problem of modeling dependencies between utterances in multi-party conversations by proposing a generic framework using dependency parsing, which boosted Transformer-based models and achieved comparable or superior performance to state-of-the-art methods on four benchmarks.
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.