Papers › More than Text: Multi-modal Chinese Word Segmentation
More than Text: Multi-modal Chinese Word Segmentation
Dong Zhang, Zheng Hu, Shoushan Li, Hanqian Wu, Qiaoming Zhu, Guodong Zhou
Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing. Previous works only focus on the textual modality, but there are often audio and video utterances (such as news broadcast and face-to-face dialogues), where textual, acoustic and visual modalities normally exist. To this end, we attempt to combine the multi-modality (mainly the converted text and actual voice information) to perform CWS. In this paper, we annotate a new dataset for CWS containing text and audio. Moreover, we propose a time-dependent multi-modal interactive model based on Transformer framework to integrate multi-modal information for word sequence labeling. The experimental results on three different training sets show the effectiveness of our approach with fusing text and audio.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections