Slot Filling with Weighted Multi-Encoders for Out-of-Domain Values.

TLDR: A new method for slot filling of out-ofdomain (OOD) slot values, which are not included in the training data, in spoken dialogue systems, using two encoders, which distinctly encode contexts and keywords, respectively.

Abstract: This paper proposes a new method for slot filling of out-ofdomain (OOD) slot values, which are not included in the training data, in spoken dialogue systems. Word embeddings have been proposed to estimate the OOD slot values included in the word embedding model from keyword information. At the same time, context information is an important clue for estimation because the values in a given slot tend to appear in similar contexts. The proper use of either or both keyword and context information depends on the sentence. Conventional methods input a whole sentence into an encoder and extract important clues by the attention mechanism. However, it is difficult to properly distinguish context and keyword information from the encoder outputs because these two features are already mixed. Our proposed method uses two encoders, which distinctly encode contexts and keywords, respectively. The model calculates weights for the two encoders based on a user utterance and estimates a slot with weighted outputs from the two encoders. Experimental results show that the proposed method achieves a 50% relative improvement in F1 score compared with a baseline model, which detects slot values from user utterances and estimates slots at once with a single encoder.