Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model
This addresses the problem of data scarcity in seq2seq training for NLP practitioners, but it is incremental as it adapts existing fusion ideas to the Transformer architecture.
The paper tackles the challenge of integrating an external language model into Transformer-based sequence-to-sequence models to leverage unpaired data when paired data is scarce, achieving better performance than conventional fusion methods in text-style conversion tasks.
This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequence-to-sequence (seq2seq) model. While paired data are basically required to train the seq2seq model, the external LM can be trained with only unpaired data. Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data. However, the existing fusion methods assume that the LM is integrated with recurrent neural network-based seq2seq models instead of the Transformer. Therefore, this paper proposes a fusion method that can explicitly utilize network structures in the Transformer. The proposed method, called {\bf memory attentive fusion}, leverages the Transformer-style attention mechanism that repeats source-target attention in a multi-hop manner for reading the memorized knowledge in the LM. Our experiments on two text-style conversion tasks demonstrate that the proposed method performs better than conventional fusion methods.