  This work aims to create a multimodal AI system that chats with humans and
shares relevant photos. While earlier works were limited to dialogues about
specific objects or scenes within images, recent works have incorporated images
into open-domain dialogues. However, their response generators are unimodal,
accepting text input but no image input, thus prone to generating responses
contradictory to the images shared in the dialogue. Therefore, this work
proposes a complete chatbot system using two multimodal deep learning models:
an image retriever that understands texts and a response generator that
understands images. The image retriever, implemented by ViT and BERT, selects
the most relevant image given the dialogue history and a database of images.
The response generator, implemented by ViT and GPT-2/DialoGPT, generates an
appropriate response given the dialogue history and the most recently retrieved
image. The two models are trained and evaluated on PhotoChat, an open-domain
dialogue dataset in which a photo is shared in each session. In automatic
evaluation, the proposed image retriever outperforms existing baselines VSE++
and SCAN with Recall@1/5/10 of 0.1/0.3/0.4 and MRR of 0.2 when ranking 1,000
images. The proposed response generator also surpasses the baseline Divter with
PPL of 16.9, BLEU-1/2 of 0.13/0.03, and Distinct-1/2 of 0.97/0.86, showing a
significant improvement in PPL by -42.8 and BLEU-1/2 by +0.07/0.02. In human
evaluation with a Likert scale of 1-5, the complete multimodal chatbot system
receives higher image-groundedness of 4.3 and engagingness of 4.3, along with
competitive fluency of 4.1, coherence of 3.9, and humanness of 3.1, when
compared to other chatbot variants. The source code is available at:
https://github.com/minniie/multimodal_chat.git.
