most citedMUG: A General Meeting Understanding and Generation Benchmark

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cs.CL202413 cited

Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems

Tianyu Cui, Yanling Wang, Chuanpu Fu +13

Large language models (LLMs) have strong capabilities in solving diverse natural language processing tasks. However, the safety and security issues of LLM systems have become the m…

cs.CL20231 cited

Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling

Hai Yu, Chong Deng, Qinglin Zhang +3

Topic segmentation is critical for obtaining structured documents and improving downstream tasks such as information retrieval. Due to its ability of automatically exploring clues…

cs.CL2023

Improving BERT with Hybrid Pooling Network and Drop Mask

Qian Chen, Wen Wang, Qinglin Zhang +3

Transformer-based pre-trained language models, such as BERT, achieve great success in various natural language understanding tasks. Prior research found that BERT captures a rich h…

cs.CL2023

Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization

Luyao Cheng, Siqi Zheng, Zhang Qinglin +3

Speaker diarization(SD) is a classic task in speech processing and is crucial in multi-party scenarios such as meetings and conversations. Current mainstream speaker diarization ap…

cs.CL2023

Meeting Action Item Detection with Regularized Context Modeling

Jiaqing Liu, Chong Deng, Qinglin Zhang +2

Meetings are increasingly important for collaborations. Action items in meeting transcripts are crucial for managing post-meeting to-do tasks, which usually are summarized laboriou…

cs.CL20231 cited

MUG: A General Meeting Understanding and Generation Benchmark

Qinglin Zhang, Chong Deng, Jiaqing Liu +7

Listening to long video/audio recordings from video conferencing and online courses for acquiring information is extremely inefficient. Even after ASR systems transcribe recordings…