35 research outputs found
Commonsense Reasoning for Conversational AI: A Survey of the State of the Art
Large, transformer-based pretrained language models like BERT, GPT, and T5
have demonstrated a deep understanding of contextual semantics and language
syntax. Their success has enabled significant advances in conversational AI,
including the development of open-dialogue systems capable of coherent, salient
conversations which can answer questions, chat casually, and complete tasks.
However, state-of-the-art models still struggle with tasks that involve higher
levels of reasoning - including commonsense reasoning that humans find trivial.
This paper presents a survey of recent conversational AI research focused on
commonsense reasoning. The paper lists relevant training datasets and describes
the primary approaches to include commonsense in conversational AI. The paper
also discusses benchmarks used for evaluating commonsense in conversational AI
problems. Finally, the paper presents preliminary observations of the limited
commonsense capabilities of two state-of-the-art open dialogue models,
BlenderBot3 and LaMDA, and its negative effect on natural interactions. These
observations further motivate research on commonsense reasoning in
conversational AI.Comment: Accepted to Workshop on Knowledge Augmented Methods for Natural
Language Processing, in conjunction with AAAI 202