Incomplete Utterance Rewriting as Sequential Greedy Tagging
This work addresses a key bottleneck in dialogue systems for improving conversational AI, though it appears incremental as it builds on existing sequence tagging approaches.
The paper tackles the problem of incomplete utterance rewriting in dialogue systems by proposing a sequence tagging-based model with speaker-aware embeddings, achieving optimal results on all nine restoration scores across multiple datasets while improving inference speed.
The task of incomplete utterance rewriting has recently gotten much attention. Previous models struggled to extract information from the dialogue context, as evidenced by the low restoration scores. To address this issue, we propose a novel sequence tagging-based model, which is more adept at extracting information from context. Meanwhile, we introduce speaker-aware embedding to model speaker variation. Experiments on multiple public datasets show that our model achieves optimal results on all nine restoration scores while having other metric scores comparable to previous state-of-the-art models. Furthermore, benefitting from the model's simplicity, our approach outperforms most previous models on inference speed.