面向联合意图检测与槽填充的任务条件化BERT
Task Conditioned BERT for Joint Intent Detection and Slot-filling
AI总结：
本文提出任务条件化BERT，通过在统一Transformer编码器上同时条件化意图和多种槽位推断，在MultiWOZ上使联合意图与槽位检测最高提升14.4%，并在真实对话中取得较高性能。
摘要：
对话系统需要应对用户意图的不可预测性以跟踪对话状态，并处理槽位的异质性以理解用户偏好。本文研究如下假设：将这些挑战作为一个统一模型来解决，将允许在不同任务之间传递参数支持数据。所提出的原则性模型基于一个Transformer编码器，在多个任务上训练，并利用丰富的输入使模型以目标推断为条件。在相同语料上以多个目标推断（即意图和多种槽位类型）为条件来条件化Transformer编码器，能够学习比单任务模型更丰富的语言交互。事实上，实验结果表明，以越来越多的对话推断任务为条件来条件化模型会带来更好的结果：在MultiWOZ数据集上，联合意图和槽位检测通过以意图为条件可提升3.2%，通过以槽位为条件可提升10.8%，通过同时以意图和槽位为条件可提升14.4%。此外，在与Farfetch客户的真实对话中，所提出的条件化BERT能够在整个对话过程中取得较高的联合目标和意图检测性能。
英文摘要：
Dialogue systems need to deal with the unpredictability of user intents to track dialogue state and the heterogeneity of slots to understand user preferences. In this paper we investigate the hypothesis that solving these challenges as one unified model will allow the transfer of parameter support data across the different tasks. The proposed principled model is based on a Transformer encoder, trained on multiple tasks, and leveraged by a rich input that conditions the model on the target inferences. Conditioning the Transformer encoder on multiple target inferences over the same corpus, i.e., intent and multiple slot types, allows learning richer language interactions than a single-task model would be able to. In fact, experimental results demonstrate that conditioning the model on an increasing number of dialogue inference tasks leads to improved results: on the MultiWOZ dataset, the joint intent and slot detection can be improved by 3.2\% by conditioning on intent, 10.8\% by conditioning on slot and 14.4\% by conditioning on both intent and slots. Moreover, on real conversations with Farfetch costumers, the proposed conditioned BERT can achieve high joint-goal and intent detection performance throughout a dialogue.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
