基于矢量量化变分自编码器的证据意识推理文本生成
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
摘要：
从不同的角度生成关于事件的推理文本需要对事件发生的不同上下文进行推理。现有的作品通常忽略了上下文，没有明确提供，导致上下文无关的语义表示，努力支持的一代。为了解决这个问题，我们提出了一种方法，自动发现证据的事件从一个大型的文本语料库，并利用证据来指导推理文本的生成。我们的方法以编码器-解码器的方式工作，并配备了矢量量化-变分自编码器，其中编码器输出离散变量分布的表示。这种离散表示使得能够自动选择相关证据，这不仅有助于证据感知生成，而且还提供了一种自然的方式来揭示生成背后的原理。我们的方法在Event 2 Mind和ATOMIC数据集上都提供了最先进的性能。更重要的是，我们发现，通过离散表示，我们的模型选择性地使用证据来生成不同的推理文本。
英文摘要：
Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoder-decoder manner and is equipped with a Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2Mind and ATOMIC datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.
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。
