Smoothing Dialogue States for Open Conversational Machine Reading
This work addresses noisy information transmission in conversational AI for more realistic open-domain settings, representing an incremental improvement.
The paper tackles the problem of noisy background knowledge in open conversational machine reading by smoothing dialogue states between decision making and question generation, achieving new state-of-the-art results on the OR-ShARC dataset.
Conversational machine reading (CMR) requires machines to communicate with humans through multi-turn interactions between two salient dialogue states of decision making and question generation processes. In open CMR settings, as the more realistic scenario, the retrieved background knowledge would be noisy, which results in severe challenges in the information transmission. Existing studies commonly train independent or pipeline systems for the two subtasks. However, those methods are trivial by using hard-label decisions to activate question generation, which eventually hinders the model performance. In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference. Experiments on the OR-ShARC dataset show the effectiveness of our method, which achieves new state-of-the-art results.