BAYES RISK CTC: CONTROLLABLE CTC ALIGNMENT IN SEQUENCE-TO-SEQUENCE TASKS
Authors: Jinchuan Tian, Brian Yan, Jianwei Yu, CHAO WENG, Dong Yu, Shinji Watanabe
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, the proposed BRCTC, along with a trimming approach, enables us to reduce the inference cost of offline models by up to 47% without performance degradation; BRCTC also cuts down the overall latency of online systems to an unseen level |
| Researcher Affiliation | Collaboration | Jinchuan Tian, Jianwei Yu , Chao Weng, Dong Yu Tencent AI LAB {tyriontian, tomasyu, cweng, dyu}@tencent.com Brian Yan & Shinji Watanabe Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA {byan, swatanab}@andrew.cmu.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found. |
| Open Source Code | Yes | Code release: https://github.com/espnet/espnet. |
| Open Datasets | Yes | Datasets: Experiments are mainly conducted for ASR, but MT and ST are also included. For ASR, the experiments are on Aishell-1 (Bu et al., 2017), Aishell-2 (Du et al., 2018), Wenetspeech (Zhang et al., 2022) and Librispeech (Panayotov et al., 2015). |
| Dataset Splits | Yes | Aishell-1 dev / test... Wenetspeech dev / meeting / net |
| Hardware Specification | Yes | All experiments are conducted on Nvidia V100 GPUs. All the inference jobs are conducted on Intel(R) Xeon(R) Platinum 8255C CPU (2.5GHz). |
| Software Dependencies | No | No specific version numbers for ancillary software dependencies were provided. |
| Experiment Setup | Yes | Implementation details are in appendix D and E for reproducibility. Details for the optimization and the setting for BRCTC are shown in table 7. |