通过主动知识转移学会交谈
Learn to Talk via Proactive Knowledge Transfer
摘要：
知识转移已被应用于解决各种各样的问题。例如，知识可以在任务之间转移（例如，通过利用现有知识学习处理新情况）或在代理之间（例如，从别人那里学习，没有直接的经验）。不失一般性，我们将知识转移与KL-发散最小化联系起来，即，匹配学习者和教师的（信念）分布。这种对等性为我们理解KL-分歧的变体提供了一个新的视角，即通过观察学习者如何构建他们与教师的互动来获得知识。在本文中，我们提供了一个深入的分析，KL发散最小化在向前和向后的顺序，这表明，学习者通过同策略学习在向后加强。相反，学习者在Forward中受到监督。此外，我们的分析是基于梯度的，所以它可以推广到任意的任务，并帮助决定最小化任务的属性。通过在知识蒸馏中用向后替换向前，我们在WMT'17 De-En和IWMT'15 Th-En机器翻译任务上观察到+0.7-1.1 BLEU增益。
英文摘要：
Knowledge Transfer has been applied in solving a wide variety of problems. For example, knowledge can be transferred between tasks (e.g., learning to handle novel situations by leveraging prior knowledge) or between agents (e.g., learning from others without direct experience). Without loss of generality, we relate knowledge transfer to KL-divergence minimization, i.e., matching the (belief) distributions of learners and teachers. The equivalence gives us a new perspective in understanding variants of the KL-divergence by looking at how learners structure their interaction with teachers in order to acquire knowledge. In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward. In contrast, learners are supervised in Forward. Moreover, our analysis is gradient-based, so it can be generalized to arbitrary tasks and help to decide which order to minimize given the property of the task. By replacing Forward with Backward in Knowledge Distillation, we observed +0.7-1.1 BLEU gains on the WMT'17 De-En and IWSLT'15 Th-En machine translation tasks.
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。
