Comments (2)
Conventional pipeline term in spoken language understanding indicates the system first generates transcription using ASR and then follows the NLP model to generate the final output. Thus, each module is trained with different objectives. An end-to-end system means you have a single model that is trained with a single objective (e.g. NER in this case). As long as you train the model in an end-to-end manner (audio or audio feature to label directly), we can regard it as an end-to-end model. Pre-training+fine-tuning can be regarded as an end-to-end system.
Submission link will be updated soon in this page.
https://asappresearch.github.io/slue-toolkit/interspeech2023_submission.html
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Related Issues (19)
- Problems to run ASR baselines HOT 1
- Voxceleb evaluation HOT 1
- Text NER baseline issues HOT 6
- Issues with baseline scripts HOT 2
- Plans to release ASR finetuned-models HOT 1
- About submission HOT 1
- Command to run E2E model of NER directly/only by speech model? HOT 1
- Save N-Best Hypotheses from Finetuned ASR for Sentiment Classification
- Inconsistent use of tabs and spaces HOT 1
- wav2letter installation error! HOT 2
- sentiment classes mismatch in fine-tune and dev tsvs HOT 1
- preprocessed manifest files not on "assumed" location HOT 2
- problem with prepare_voxpopuli.py HOT 5
- Key 'label_dir' not in 'AudioClassificationConfig' HOT 4
- Dataset Indexing Issue
- Reproduce e2e NER experiments HOT 2
- Text NER Evaluation Pipeline doesn't seem to work HOT 2
- Sentiment Analysis baseline HOT 7
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