Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

TL;DR

MaChAmp is a versatile toolkit designed to facilitate multi-task learning in NLP by enabling easy fine-tuning of pre-trained contextualized embeddings across various NLP tasks.

Contribution

It introduces a flexible, unified toolkit that supports multiple NLP tasks for efficient multi-task learning and fine-tuning of contextualized embeddings.

Findings

Supports a wide range of NLP tasks

Flexible configuration options

Simplifies multi-task fine-tuning process

Abstract

Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years. In this paper we present MaChAmp, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings. The benefits of MaChAmp are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation.