We consider a novel question answering (QA) task where the machine needs to
read from large streaming data (long documents or videos) without knowing when
the questions will be given, which is difficult to solve with existing QA
methods due to their lack of scalability. To tackle this problem, we propose a
novel end-to-end deep network model for reading comprehension, which we refer
to as Episodic Memory Reader (EMR) that sequentially reads the input contexts
into an external memory, while replacing memories that are less important for
answering \emph{unseen} questions. Specifically, we train an RL agent to
replace a memory entry when the memory is full, in order to maximize its QA
accuracy at a future timepoint, while encoding the external memory using either
the GRU or the Transformer architecture to learn representations that considers
relative importance between the memory entries. We validate our model on a
synthetic dataset (bAbI) as well as real-world large-scale textual QA
(TriviaQA) and video QA (TVQA) datasets, on which it achieves significant
improvements over rule-based memory scheduling policies or an RL-based baseline
that independently learns the query-specific importance of each memory.

本文提出了一个基于序列阅读的外部内存读取模型 Episodic Memory Reader, 并结合强化学习代理进行记忆筛选，用于在大规模数据流中进行问答。实验结果表明该模型在文本 QA 和视频 QA 任务中相较于传统记忆调度策略或独立序列学习的强化学习 baseline 有了更好的表现。