Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
This work provides a better understanding of encoder layer fusion for researchers and practitioners working with sequence-to-sequence models, leading to a more efficient and effective fusion method.
This paper investigates EncoderFusion in sequence-to-sequence models, finding that the encoder embedding layer is more crucial than intermediate layers. Based on this, they propose SurfaceFusion, which fuses only the encoder embedding layer and achieves state-of-the-art performance on WMT16 Romanian-English and WMT14 English-French translation tasks, outperforming EncoderFusion.
Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks. However, it is still not entirely clear why and when EncoderFusion should work. In this paper, our main contribution is to take a step further in understanding EncoderFusion. Many of previous studies believe that the success of EncoderFusion comes from exploiting surface and syntactic information embedded in lower encoder layers. Unlike them, we find that the encoder embedding layer is more important than other intermediate encoder layers. In addition, the uppermost decoder layer consistently pays more attention to the encoder embedding layer across NLP tasks. Based on this observation, we propose a simple fusion method, SurfaceFusion, by fusing only the encoder embedding layer for the softmax layer. Experimental results show that SurfaceFusion outperforms EncoderFusion on several NLP benchmarks, including machine translation, text summarization, and grammatical error correction. It obtains the state-of-the-art performance on WMT16 Romanian-English and WMT14 English-French translation tasks. Extensive analyses reveal that SurfaceFusion learns more expressive bilingual word embeddings by building a closer relationship between relevant source and target embedding. Source code is freely available at https://github.com/SunbowLiu/SurfaceFusion.