AutoNMT: A Framework to Streamline the Research of Seq2Seq Models
This framework addresses inefficiencies in seq2seq research workflows for machine learning practitioners, though it is incremental as it builds on existing toolkits and automation concepts.
The authors tackled the challenge of streamlining research on sequence-to-sequence models by developing AutoNMT, a framework that automates data pipeline management, experimentation, and report generation, resulting in a toolkit-agnostic solution that supports custom models and existing toolkits like Fairseq or OpenNMT.
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.