Dramatic Conversation Disentanglement
Dramatic Conversation Disentanglement
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DOI：
10.48550/arxiv.2305.16648
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Kent K. Chang;Danica Chen;David Bamman
中科院分区：
文献类型：
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作者：
Kent K. Chang;Danica Chen;David Bamman
我们提出了一个新的数据集，用于研究影视剧中的会话解缠。虽然以往的研究主要集中在IRC聊天室对话中的会话解缠，但电影和电视节目为研究面对面多方互动中发言权和话题转换的复杂语用模式提供了空间。在这项工作中，我们利用社会语言学、社会学和电影研究的理论研究来操作戏剧文本中的对话线索(包括发言权改变的概念)，并使用这个定义来注释来自831部电影的10,033个对话轮次(包括2,209个线索)的数据集。我们在这个戏剧性的数据集上比较了几种解缠模型的性能，并将性能最好的模型应用于808部电影的解缠。我们看到，与预期相反，在过去的40年里，平均线索长度并没有显著减少，而由女性演员扮演的角色虽然代表不足，但相对于他们的发言时间，引发了更多新的对话线索。
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.