Twitter has emerged as a global hub for engaging in online conversations and
as a research corpus for various disciplines that have recognized the
significance of its user-generated content. Argument mining is an important
analytical task for processing and understanding online discourse.
Specifically, it aims to identify the structural elements of arguments, denoted
as information and inference. These elements, however, are not static and may
require context within the conversation they are in, yet there is a lack of
data and annotation frameworks addressing this dynamic aspect on Twitter. We
contribute TACO, the first dataset of Twitter Arguments utilizing 1,814 tweets
covering 200 entire conversations spanning six heterogeneous topics annotated
with an agreement of 0.718 Krippendorff's alpha among six experts. Second, we
provide our annotation framework, incorporating definitions from the Cambridge
Dictionary, to define and identify argument components on Twitter. Our
transformer-based classifier achieves an 85.06\% macro F1 baseline score in
detecting arguments. Moreover, our data reveals that Twitter users tend to
engage in discussions involving informed inferences and information. TACO
serves multiple purposes, such as training tweet classifiers to manage tweets
based on inference and information elements, while also providing valuable
insights into the conversational reply patterns of tweets.

通过涵盖 6 个异构主题、约 200 个完整对话、1814 条推文的 TACO 数据集，结合剑桥词典的定义，该研究构建了 Twitter 争论的注释框架和基于 transformer 的分类器，揭示了 Twitter 用户倾向于涉及独立推断和信息的讨论，并提供了有关推文交谈回复模式的有价值洞察。

TACO -- 来自对话的 Twitter 论点

TACO -- Twitter Arguments from COnversations

Humans commonly work with multiple objects in daily life and can intuitively
transfer manipulation skills to novel objects by understanding object
functional regularities. However, existing technical approaches for analyzing
and synthesizing hand-object manipulation are mostly limited to handling a
single hand and object due to the lack of data support. To address this, we
construct TACO, an extensive bimanual hand-object-interaction dataset spanning
a large variety of tool-action-object compositions for daily human activities.
TACO contains 2.5K motion sequences paired with third-person and egocentric
views, precise hand-object 3D meshes, and action labels. To rapidly expand the
data scale, we present a fully-automatic data acquisition pipeline combining
multi-view sensing with an optical motion capture system. With the vast
research fields provided by TACO, we benchmark three generalizable
hand-object-interaction tasks: compositional action recognition, generalizable
hand-object motion forecasting, and cooperative grasp synthesis. Extensive
experiments reveal new insights, challenges, and opportunities for advancing
the studies of generalizable hand-object motion analysis and synthesis. Our
data and code are available at this https URL

通过构建 TACO 数据集，该研究解决了现有技术方法对于处理单个手和物体的手 - 物体操作分析和合成的局限性，并通过三个可推广的手 - 物体交互任务（组合动作识别、可推广的手 - 物体运动预测和合作抓取合成）来提供广泛的研究领域，从而为推进可推广的手 - 物体运动分析和合成研究提供了新的见解、挑战和机遇。

TACO：通用双手工具 - 动作 - 对象理解的基准评估

TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object  Understanding

In this paper, we study multi-label atomic activity recognition. Despite the
notable progress in action recognition, it is still challenging to recognize
atomic activities due to a deficiency in a holistic understanding of both
multiple road users' motions and their contextual information. In this paper,
we introduce Action-slot, a slot attention-based approach that learns visual
action-centric representations, capturing both motion and contextual
information. Our key idea is to design action slots that are capable of paying
attention to regions where atomic activities occur, without the need for
explicit perception guidance. To further enhance slot attention, we introduce a
background slot that competes with action slots, aiding the training process in
avoiding unnecessary focus on background regions devoid of activities. Yet, the
imbalanced class distribution in the existing dataset hampers the assessment of
rare activities. To address the limitation, we collect a synthetic dataset
called TACO, which is four times larger than OATS and features a balanced
distribution of atomic activities. To validate the effectiveness of our method,
we conduct comprehensive experiments and ablation studies against various
action recognition baselines. We also show that the performance of multi-label
atomic activity recognition on real-world datasets can be improved by
pretraining representations on TACO. We will release our source code and
dataset. See the videos of visualization on the project page:
this https URL

我们研究了多标签原子活动识别，引入了基于槽的关注方法来学习视觉的动作中心表示，并通过在 TACO 数据集上进行预训练来提高多标签原子活动识别在真实数据集中的性能。