Graph Representations for Machine Translation in Dialogue Settings

In this paper, we present our approach to the WMT24 - Chat Task, addressing the challenge of translating chat conversations.Chat conversations are characterised by their informal, ungrammatical nature and strong reliance on context posing significant challenges for machine translation systems. To address these challenges, we augment large language models with explicit memory mechanisms designed to enhance coherence and consistency across dialogues. Specifically, we employ graph representations to capture and utilise dialogue context, leveraging concept connectivity as a compressed memory. Our approach ranked second place for Dutch and French, and third place for Portuguese and German, based on COMET-22 scores and human evaluation.