论文标题
我喜欢鱼，尤其是海豚：解决对话建模中的矛盾
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling
论文作者
论文摘要
为了量化自然语言理解模型能够在一般对话中捕获一致性，我们介绍了对话构成检测任务（解码）和一个包含人类和人类矛盾的对话的新对话数据集。然后，我们比较一种基于结构化话语的方法，即使用预训练的变压器模型与典型的非结构化方法进行矛盾检测。结果表明：（i）我们新收集的数据集比现有的NLI数据（包括旨在涵盖对话域的数据）更有效地为对话构成检测任务提供监督； （ii）基于结构化的话语方法比其非结构化对应物在分析和分布对话中更强大，可以转移。我们还表明，我们的最佳矛盾检测模型与人类判断良好相关，并进一步提供了其在自动评估和改善最新生成聊天机器人一致性时使用的证据。
To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We then compare a structured utterance-based approach of using pre-trained Transformer models for contradiction detection with the typical unstructured approach. Results reveal that: (i) our newly collected dataset is notably more effective at providing supervision for the dialogue contradiction detection task than existing NLI data including those aimed to cover the dialogue domain; (ii) the structured utterance-based approach is more robust and transferable on both analysis and out-of-distribution dialogues than its unstructured counterpart. We also show that our best contradiction detection model correlates well with human judgments and further provide evidence for its usage in both automatically evaluating and improving the consistency of state-of-the-art generative chatbots.
