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

Learning meaningful and compact representations with disentangled semantic
aspects is considered to be of key importance in representation learning. Since
real-world data is notoriously costly to collect, many recent state-of-the-art
disentanglement models have heavily relied on synthetic toy data-sets. In this
paper, we propose a novel data-set which consists of over one million images of
physical 3D objects with seven factors of variation, such as object color,
shape, size and position. In order to be able to control all the factors of
variation precisely, we built an experimental platform where the objects are
being moved by a robotic arm. In addition, we provide two more datasets which
consist of simulations of the experimental setup. These datasets provide for
the first time the possibility to systematically investigate how well different
disentanglement methods perform on real data in comparison to simulation, and
how simulated data can be leveraged to build better representations of the real
world. We provide a first experimental study of these questions and our results
indicate that learned models transfer poorly, but that model and hyperparameter
selection is an effective means of transferring information to the real world.

本文提出了一个包含超过一百万张 3D 物体图像的数据集，以及在实验平台上运用机械臂移动物体的方式，以此来精确控制所有变化的因素，实现了不同解缠绕建模方法对模拟数据和真实数据的比对，研究结果表明模型选择是从模拟数据向真实数据传递信息的有效手段。