Implicit Discourse Relation Recognition (IDRR) is a sophisticated and
challenging task to recognize the discourse relations between the arguments
with the absence of discourse connectives. The sense labels for each discourse
relation follow a hierarchical classification scheme in the annotation process
(Prasad et al., 2008), forming a hierarchy structure. Most existing works do
not well incorporate the hierarchy structure but focus on the syntax features
and the prior knowledge of connectives in the manner of pure text
classification. We argue that it is more effective to predict the paths inside
the hierarchical tree (e.g., "Comparison -> Contrast -> however") rather than
flat labels (e.g., Contrast) or connectives (e.g., however). We propose a
prompt-based path prediction method to utilize the interactive information and
intrinsic senses among the hierarchy in IDRR. This is the first work that
injects such structure information into pre-trained language models via prompt
tuning, and the performance of our solution shows significant and consistent
improvement against competitive baselines.

本文提出了一种基于提示的路径预测方法来识别隐性的篇章关系，此方法利用了层级结构的信息并加以利用于预先训练好的语言模型中，从而在隐性篇章关系的识别上取得显著的性能提升。

DiscoPrompt：隐性语篇关系识别中的路径预测提示调优

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse  Relation Recognition

This paper addresses the problem of path prediction for multiple interacting
agents in a scene, which is a crucial step for many autonomous platforms such
as self-driving cars and social robots. We present \textit{SoPhie}; an
interpretable framework based on Generative Adversarial Network (GAN), which
leverages two sources of information, the path history of all the agents in a
scene, and the scene context information, using images of the scene. To predict
a future path for an agent, both physical and social information must be
leveraged. Previous work has not been successful to jointly model physical and
social interactions. Our approach blends a social attention mechanism with a
physical attention that helps the model to learn where to look in a large scene
and extract the most salient parts of the image relevant to the path. Whereas,
the social attention component aggregates information across the different
agent interactions and extracts the most important trajectory information from
the surrounding neighbors. SoPhie also takes advantage of GAN to generates more
realistic samples and to capture the uncertain nature of the future paths by
modeling its distribution. All these mechanisms enable our approach to predict
socially and physically plausible paths for the agents and to achieve
state-of-the-art performance on several different trajectory forecasting
benchmarks.

本文通过 SoPhie，基于生成对抗网络的可解释框架，使模型能够联合建模物理和社交互动，从而在多个自主平台上预测多个交互代理在场景中的路径。

SoPhie：一种注意力增强的生成对抗网络，用于预测符合社交和物理限制的路径

SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and  Physical Constraints

Recurrent neural networks are able to learn complex long-term relationships
from sequential data and output a pdf over the state space. Therefore,
recurrent models are a natural choice to address path prediction tasks, where a
trained model is used to generate future expectations from past observations.
When applied to security applications, like predicting the path of pedestrians
for risk assessment, a point-wise greedy (ML) evaluation of the output pdf is
not feasible, since the environment often allows multiple choices. Therefore, a
robust risk assessment has to take all options into account, even if they are
overall not very likely.
Towards this end, a combination of particle filter sampling strategies and a
LSTM-MDL model is proposed to address a multi-modal path prediction task. The
capabilities and viability of the proposed approach are evaluated on several
synthetic test conditions, yielding the counter-intuitive result that the
simplest approach performs best. Further, the feasibility of the proposed
approach is illustrated on several real world scenes.

提出了一种结合粒子滤波采样策略和 LSTM-MDL 模型的多模式路径预测方法，可用于安全应用中的风险评估，通过在多种现实场景中进行的实验表明最简单的方法表现最佳。

基于 LSTM-MDL 模型的基于粒子的行人路径预测

Particle-based pedestrian path prediction using LSTM-MDL models

We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.

提出了利用空间导航环境和行为之间的依赖关系进行路径预测的可解释框架，利用 Clairvoyant Attentive Recurrent Network（CAR-Net）解决路径预测任务，并成功地应用于 Formula One 赛道、Stanford Drone 数据集等多个数据集上，并具有良好的泛化能力。