DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator
This work improves response generation in multimodal dialog systems, but it is incremental as it builds on existing transformer-based architectures.
The paper tackled the problem of generating responses in Audio Visual Scene-aware Dialog (AVSD) by addressing overfitting to grammatical patterns and prior vocabulary distributions, proposing a Multimodal Semantic Transformer Network that outperformed most previous works.
Audio Visual Scene-aware Dialog (AVSD) is the task of generating a response for a question with a given scene, video, audio, and the history of previous turns in the dialog. Existing systems for this task employ the transformers or recurrent neural network-based architecture with the encoder-decoder framework. Even though these techniques show superior performance for this task, they have significant limitations: the model easily overfits only to memorize the grammatical patterns; the model follows the prior distribution of the vocabularies in a dataset. To alleviate the problems, we propose a Multimodal Semantic Transformer Network. It employs a transformer-based architecture with an attention-based word embedding layer that generates words by querying word embeddings. With this design, our model keeps considering the meaning of the words at the generation stage. The empirical results demonstrate the superiority of our proposed model that outperforms most of the previous works for the AVSD task.