Interactive Machine Learning (IML) shall enable intelligent systems to interactively learn from their end-users, and is quickly becoming more and more important. Although it puts the human in the loop, interactions are mostly performed via mutual explanations that miss contextual information. Furthermore, current model-agnostic IML strategies like CAIPI are limited to 'destructive' feedback, meaning they solely allow an expert to prevent a learner from using irrelevant features. In this work, we propose a novel interaction framework called Semantic Interactive Learning for the text domain. We frame the problem of incorporating constructive and contextual feedback into the learner as a task to find an architecture that (a) enables more semantic alignment between humans and machines and (b) at the same time helps to maintain statistical characteristics of the input domain when generating user-defined counterexamples based on meaningful corrections. Therefore, we introduce a technique called SemanticPush that is effective for translating conceptual corrections of humans to non-extrapolating training examples such that the learner's reasoning is pushed towards the desired behavior. In several experiments, we show that our method clearly outperforms CAIPI, a state of the art IML strategy, in terms of Predictive Performance as well as Local Explanation Quality in downstream multi-class classification tasks.
翻译:交互式机器学习(IML) 将使智能系统能够向终端用户进行互动学习,并迅速变得日益重要。 虽然它让人类进入循环圈, 互动大多是通过相互解释来进行, 缺少背景信息。 此外, CPAIP 等当前模范、 不可知的IML 战略仅限于“ 破坏性” 反馈, 意味着它们只允许专家防止学习者使用不相干的特点。 在这项工作中, 我们提议了一个名为“ 语义互动学习” 的新互动框架, 用于文本域。 我们把将建设性和背景反馈纳入学习者的问题设定为一项任务, 以寻找一个(a) 能够使人和机器之间更具有语义性一致性的架构, 以及(b) 同时帮助在产生用户定义的反比标本时保持输入域的统计特征。 因此, 我们引入了一种叫作“ 语义Push” 的技术, 来有效地将人类的概念校正转化为非排斥性的培训范例, 比如, 学习者的理论推向理想的行为。 在几个实验中, 我们展示了我们的方法明显超越了 CAIPI 和 IML 的下游阶段 战略的业绩, 。