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pro vyhledávání: '"Topic segmentation"'
Dialogue topic segmentation plays a crucial role in various types of dialogue modeling tasks. The state-of-the-art unsupervised DTS methods learn topic-aware discourse representations from conversation data through adjacent discourse matching and pse
Externí odkaz:
http://arxiv.org/abs/2409.07672
Recent advancements in speech-based topic segmentation have highlighted the potential of pretrained speech encoders to capture semantic representations directly from speech. Traditionally, topic segmentation has relied on a pipeline approach in which
Externí odkaz:
http://arxiv.org/abs/2409.06222
The video topic segmentation (VTS) task segments videos into intelligible, non-overlapping topics, facilitating efficient comprehension of video content and quick access to specific content. VTS is also critical to various downstream video understand
Externí odkaz:
http://arxiv.org/abs/2408.00365
From organizing recorded videos and meetings into chapters, to breaking down large inputs in order to fit them into the context window of commoditized Large Language Models (LLMs), topic segmentation of large transcripts emerges as a task of increasi
Externí odkaz:
http://arxiv.org/abs/2407.12028
The advancement of large language models (LLMs) has propelled the development of dialogue systems. Unlike the popular ChatGPT-like assistant model, which only satisfies the user's preferences, task-oriented dialogue systems have also faced new requir
Externí odkaz:
http://arxiv.org/abs/2405.19799
Autor:
Decker, Amandine, Amblard, Maxime
Publikováno v:
CODI 2024 - 5th workshop on Computational Approaches to Discourse, Mar 2024, Malta, Malta
Topics play an important role in the global organisation of a conversation as what is currently discussed constrains the possible contributions of the participant. Understanding the way topics are organised in interaction would provide insight on the
Externí odkaz:
http://arxiv.org/abs/2402.02837
Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models
Autor:
Ghosh, Reshmi, Kajal, Harjeet Singh, Kamath, Sharanya, Shrivastava, Dhuri, Basu, Samyadeep, Zeng, Hansi, Srinivasan, Soundararajan
Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many downstream tasks. However, current works on topic segmentation often fo
Externí odkaz:
http://arxiv.org/abs/2310.17120
Topic segmentation is critical for obtaining structured documents and improving downstream tasks such as information retrieval. Due to its ability of automatically exploring clues of topic shift from abundant labeled data, recent supervised neural mo
Externí odkaz:
http://arxiv.org/abs/2310.11772
Autor:
Xing, Linzi, Tran, Quan, Caba, Fabian, Dernoncourt, Franck, Yoon, Seunghyun, Wang, Zhaowen, Bui, Trung, Carenini, Giuseppe
Video topic segmentation unveils the coarse-grained semantic structure underlying videos and is essential for other video understanding tasks. Given the recent surge in multi-modal, relying solely on a single modality is arguably insufficient. On the
Externí odkaz:
http://arxiv.org/abs/2312.00220
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