Keyan Zhou


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Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference !
Zecheng Tang | Pinzheng Wang | Keyan Zhou | Juntao Li | Ziqiang Cao | Min Zhang
Findings of the Association for Computational Linguistics: ACL 2023

Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space. However, there exist nonnegligible gaps between training and inference, owing to the absence of the forward process during inference. Thus, the model only predicts based on the previously generated reverse noise rather than the noise computed by the forward process. Besides, the widely-used downsampling strategy in speeding up the inference will cause the mismatch of diffusion trajectories between training and inference. To understand and mitigate the above two types of training-inference discrepancies, we launch a thorough preliminary study. Based on our observations, we propose two simple yet effective methods to bridge the gaps mentioned above, named Distance Penalty and Adaptive Decay Sampling. Extensive experiments on 6 generation tasks confirm the superiority of our methods, which can achieve 100× → 200× speedup with better performance. Our code will be released at


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CASIA-CASSIL: a Chinese Telephone Conversation Corpus in Real Scenarios with Multi-leveled Annotation
Keyan Zhou | Aijun Li | Zhigang Yin | Chengqing Zong
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

CASIA-CASSIL is a large-scale corpus base of Chinese human-human naturally-occurring telephone conversations in restricted domains. The first edition consists of 792 90-second conversations belonging to tourism domain, which are selected from 7,639 spontaneous telephone recordings in real scenarios. The corpus is now being annotated with wide range of linguistic and paralinguistic information in multi-levels. The annotations include Turns, Speaker Gender, Orthographic Transcription, Chinese Syllable, Chinese Phonetic Transcription, Prosodic Boundary, Stress of Sentence, Non-Speech Sounds, Voice Quality, Topic, Dialog-act and Adjacency Pairs, Ill-formedness, and Expressive Emotion as well, 13 levels in total. The abundant annotation will be effective especially for studying Chinese spoken language phenomena. This paper describes the whole process to build the conversation corpus, including collecting and selecting the original data, and the follow-up process such as transcribing, annotating, and so on. CASIA-CASSIL is being extended to a large scale corpus base of annotated Chinese dialogs for spoken Chinese study.


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NLPR translation system for IWSLT 2006 evaluation campaign
Chunguang Chai | Jinhua Du | Wei Wei | Peng Liu | Keyan Zhou | Yanqing He | Chengqing Zong
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign