AutoNMT: A Framework to Streamline the Research of Seq2Seq Models
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Abstract (arXiv)
We present AutoNMT, a framework to streamline the research of seq-to-seq models by automating the data pipeline (i.e., file management, data preprocessing, and exploratory analysis), automating experimentation in a toolkit-agnostic manner, which allows users to use either their own models or existing seq-to-seq toolkits such as Fairseq or OpenNMT, and finally, automating the report generation (plots and summaries). Furthermore, this library comes with its own seq-to-seq toolkit so that users can easily customize it for non-standard tasks.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 9 Feb 2023.
Builds on
8 of 17 references.
References · 8
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
- 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
- 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
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Citing papers per year: 2025: 1.
1 paper.