This paper addresses the problem of dialogue reasoning with contextualized
commonsense inference. We curate CICERO, a dataset of dyadic conversations with
five types of utterance-level reasoning-based inferences: cause, subsequent
event, prerequisite, motivation, and emotional reaction. The dataset contains
53,105 of such inferences from 5,672 dialogues. We use this dataset to solve
relevant generative and discriminative tasks: generation of cause and
subsequent event; generation of prerequisite, motivation, and listener's
emotional reaction; and selection of plausible alternatives. Our results
ascertain the value of such dialogue-centric commonsense knowledge datasets. It
is our hope that CICERO will open new research avenues into commonsense-based
dialogue reasoning.

本文介绍了解决上下文中常识性推理对话推理问题的方法，并介绍了通过 CICERO 数据集解决相关机器学习生成和判别任务的结果，提出 CICERO 对话性常识知识数据集的价值，并为基于常识的对话推理开辟新的研究方向。

CICERO：对话中语境化常识推理的数据集

CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

We present a new versatile building block for deep point cloud processing
architectures that is equally suited for diverse tasks. This building block
combines the ideas of spatial transformers and multi-view convolutional
networks with the efficiency of standard convolutional layers in two and
three-dimensional dense grids. The new block operates via multiple parallel
heads, whereas each head differentiably rasterizes feature representations of
individual points into a low-dimensional space, and then uses dense convolution
to propagate information across points. The results of the processing of
individual heads are then combined together resulting in the update of point
features. Using the new block, we build architectures for both discriminative
(point cloud segmentation, point cloud classification) and generative (point
cloud inpainting and image-based point cloud reconstruction) tasks. The
resulting architectures achieve state-of-the-art performance for these tasks,
demonstrating the versatility of the new block for point cloud processing.

我们提出了一种新的深度点云处理架构的多功能建筑块，该建筑块将空间转换器和多视图卷积网络的思想与二维和三维密集网格中标准卷积层的效率相结合，实现信息的多个并行头的协同作业，以更新点特征，取得了点云处理方面生成和判别任务的最新成果。