Self-Regulated Interactive Sequence-to-Sequence Learning

Code

• github.com/StatNLP/joeynmt
  pytorch★ 1
• github.com/deep-spin/sigmorphon-seq2seq
  pytorch★ 0
• github.com/benjaminbeilharz/hierarchical-reinforcement-learning
  pytorch★ 0
• github.com/jarl93/joeynmt-modified
  pytorch★ 0
• github.com/songchujun/joeynmt
  pytorch★ 0

Abstract

Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an -greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.

Tasks

Reproductions