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Found 7 papers, 5 papers with code
Loss Masking Is Not Needed in Decoder-only Transformer for Discrete-token-based ASR
We find that applying the conventional cross-entropy loss on input speech tokens does not consistently improve the ASR performance over the Loss Masking approach.
Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling
Our approach improve $F_1$ of old SOTA by 3. 42 (73. 74 -> 77. 16) and reduces $P_k$ by 1. 11 points (15. 0 -> 13. 89) on WIKI-727K and achieves an average relative reduction of 4. 3% on $P_k$ on WikiSection.
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e. g., BERT, without fine-tuning.
MUG: A General Meeting Understanding and Generation Benchmark
To prompt SLP advancement, we establish a large-scale general Meeting Understanding and Generation Benchmark (MUG) to benchmark the performance of a wide range of SLP tasks, including topic segmentation, topic-level and session-level extractive summarization and topic title generation, keyphrase extraction, and action item detection.
Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)
ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings.
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.
Hero: On the Chaos When PATH Meets Modules
The results showed that \textsc{Hero} achieved a high detection rate of 98. 5\% on a DM issue benchmark and found 2, 422 new DM issues in 2, 356 popular Golang projects.
Software Engineering
