Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unified Segment-to-Segment Framework for Simultaneous Sequence Generation
Authors: Shaolei Zhang, Yang Feng
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple simultaneous generation tasks demonstrate that Seg2Seg achieves state-of-the-art performance and exhibits better generality across various tasks2. |
| Researcher Affiliation | Academia | 1Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) 2University of Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 illustrates the specific inference process of Seg2Seg. |
| Open Source Code | Yes | 2Code is available at: https://github.com/ictnlp/Seg2Seg. |
| Open Datasets | Yes | We apply Libri Speech3 benchmark [59], which consists of 960 hours English audio. |
| Dataset Splits | Yes | We use dev-clean (5.4 hours) and dev-other (5.3 hours) as validation sets, and test-clean (5.4 hours) and test-other (5.1 hours) as test sets, where test-other set contains more noisy audio. For speech, we use the raw 16-bit 16k Hz mono-channel audio wave. For text, we use Sentence Piece [60] to generate a unigram vocabulary of size 10000. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. It only mentions using a Transformer-Base model and pre-trained Wav2Vec2.0. |
| Software Dependencies | No | The paper mentions software like Fairseq Library [66], Wav2Vec2.0 [67], and Simul Eval [68], but it does not specify exact version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | In Seg2Seg, we use the standard Transformer-Base (6 encoder and 6 decoder layers) [55] for Simul MT. For streaming ASR and Simul ST, we replace the word embedding layer in Transformer-Base with a pre-trained Wav2Vec2.06 [67] to extract the acoustic embedding, and the rest remains the same as Simul MT. |