To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent language models trained with deep neural networks are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do. We introduce a repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently understand their partner over time. We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners. Linguistic communication depends critically on shared knowledge about the meanings of words BID9 . However, the real-world demands of communication often require speakers and listeners to go beyond dictionary meanings to understand one another BID0 BID15 . The social world continually presents new communicative challenges, and agents must continually coordinate on new meanings to meet them.For example, consider a nurse visiting a bed-ridden patient in a cluttered home. The first time they ask the nurse to retrieve a particular medication, the patient must painstakingly refer to unfamiliar pills, e.g. "the vasoprex-tecnoblek meds for my blood pressure, in a small bluish bottle, on the bookcase in my bathroom." After a week of care, however, they may just ask for their "Vasotec."This type of flexible language use poses a challenge for models of language in machine learning. Approaches based on deep neural networks typically learn a monolithic meaning function during training, with fixed weights during use. For an in-home robot to communicate as flexibly and efficiently with patients as a human nurse, it must be equipped with a continual learning mechanism. Such a mechanism would present two specific advantages for interaction and communication applications. First, to the extent that current models have difficulty communicating in a new setting, an adaptive approach can quickly improve performance on the relevant subset of language. Second, for human-robot contexts, an adaptive model enables speakers to communicate more efficiently as they build up common ground, remaining understandable while expending significantly fewer words as humans naturally do BID1 .In this paper, we introduce a benchmark communication task and general continual learning framework for transforming neural language models into adaptive models that can be deployed in real-time interactions with other agents.Our key insight is that through continual interactions with the same partner in a shared context, an adaptive listener can more effectively communicate with its partner FIG0 .We are motivated by hierarchical Bayesian approaches to task-specific adaptation. Our approach integrates two core components: (i) a loss function combining speaker and listener information, and (ii ) a regularization scheme for fine-tuning model weights without overfitting. Human language use is flexible, continuously adapting to the needs of the current situation. In this paper, we introduced a challenging repeated reference game benchmark for artificial agents, which requires such adaptability to succeed. We proposed a continual learning approach that forms context-specific conventions by adapting general-purpose semantic knowledge. Even when models based on generalpurpose knowledge perform poorly, our approach allows human speakers working with adapted variants of such models to become more accurate and more efficient over time.
We propose a repeated reference benchmark task and a regularized continual learning approach for adaptive communication with humans in unfamiliar domains
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