Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models
Shun Kiyono, Sho Takase, Jun Suzuki, Naoaki Okazaki, Kentaro Inui, Masaaki Nagata · 2018
Developing a method for understanding the inner workings of black-box neural methods is an important research endeavor.Conventionally, many studies have used an attention matrix to interpret how Encoder-Decoder-based models translate a given source sentence to the corresponding target sentence.However, recent studies have empirically revealed that an attention matrix is not optimal for token-wise translation analyses.We propose a method that explicitly models the token-wise alignment between the source and target sequences to provide a better analysis.Experiments show that our method can acquire token-wise alignments that are superior to those of an attention mechanism 1 .