We present a new dataset for studying conversation disentanglement in movies
and TV series. While previous work has focused on conversation disentanglement
in IRC chatroom dialogues, movies and TV shows provide a space for studying
complex pragmatic patterns of floor and topic change in face-to-face
multi-party interactions. In this work, we draw on theoretical research in
sociolinguistics, sociology, and film studies to operationalize a
conversational thread (including the notion of a floor change) in dramatic
texts, and use that definition to annotate a dataset of 10,033 dialogue turns
(comprising 2,209 threads) from 831 movies. We compare the performance of
several disentanglement models on this dramatic dataset, and apply the
best-performing model to disentangle 808 movies. We see that, contrary to
expectation, average thread lengths do not decrease significantly over the past
40 years, and characters portrayed by actors who are women, while
underrepresented, initiate more new conversational threads relative to their
speaking time.

本文提出了一种用于研究电影和电视剧中的对话分离的新数据集，并比较了几种分离模型在数据集上的表现。我们发现，与预期相反，过去 40 年中平均线程长度并没有显著降低，并且由女性扮演的角色相对于其发言时间更容易引发新的对话线程。

戏剧性对话解缠

Dramatic Conversation Disentanglement

Prerecorded laughter accompanying dialog in comedy TV shows encourages the
audience to laugh by clearly marking humorous moments in the show. We present
an approach for automatically detecting humor in the Friends TV show using
multimodal data. Our model is capable of recognizing whether an utterance is
humorous or not and assess the intensity of it. We use the prerecorded laughter
in the show as annotation as it marks humor and the length of the audience's
laughter tells us how funny a given joke is. We evaluate the model on episodes
the model has not been exposed to during the training phase. Our results show
that the model is capable of correctly detecting whether an utterance is
humorous 78% of the time and how long the audience's laughter reaction should
last with a mean absolute error of 600 milliseconds.

本研究使用多模态数据，通过预录笑声，自动检测《老友记》电视剧中的幽默元素，成功率为 78％，平均误差为 600 毫秒。

何时大笑以及如何大笑？一种多模态方法来检测幽默及其强度

When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity

TV shows depict a wide variety of human behaviors and have been studied
extensively for their potential to be a rich source of data for many
applications. However, the majority of the existing work focuses on 2D
recognition tasks. In this paper, we make the observation that there is a
certain persistence in TV shows, i.e., repetition of the environments and the
humans, which makes possible the 3D reconstruction of this content. Building on
this insight, we propose an automatic approach that operates on an entire
season of a TV show and aggregates information in 3D; we build a 3D model of
the environment, compute camera information, static 3D scene structure and body
scale information. Then, we demonstrate how this information acts as rich 3D
context that can guide and improve the recovery of 3D human pose and position
in these environments. Moreover, we show that reasoning about humans and their
environment in 3D enables a broad range of downstream applications:
re-identification, gaze estimation, cinematography and image editing. We apply
our approach on environments from seven iconic TV shows and perform an
extensive evaluation of the proposed system.

本文提出在整个节目季度中聚合三维信息以进行电视剧的三维重建，该信息作为丰富的三维上下文可用于指导和改善这些环境中的三维人体姿势和位置恢复，并展示了 3D 推理不仅能够应用于重新识别和注视估计，还可以应用于电影摄影与图像编辑等各种应用领域。