DialoGen: Generalized Long-Range Context Representation for Dialogue Systems

Understand

Long-range context modeling is crucial to both dialogue understanding and generation.

• The most popular method for dialogue context representation is to concatenate the last-$k$ utterances in chronological order.
• However, this method may not be ideal for conversations containing long-range dependencies, i.e., when there is a need to look beyond last-$k$ utterances to generate a meaningful response.
• In this work, we propose DialoGen, a novel encoder-decoder based framework for dialogue generation with a generalized context representation that can look beyond the last-$k$ utterances.