Dynamic neuro-symbolic integration

Data Science, AI and ML
#1

There are two main limitations to the neuro-symbolic integration discussed above:

  1. Coverage: relevant knowledge is often not found as-is in commonsense knowledge resources. As we’ve seen earlier, commonsense knowledge is immeasurably vast, so much of it is not documented.

  2. Precision and context: knowledge found in the knowledge base about concept X doesn’t necessarily apply to all contexts in which X appears. For example, when provided with " PersonX adopts a cat ", ATOMIC says that PersonX had to go to the shelter first (Figure 17), but that’s not always the case. It may as well be that PersonX adopted a cat they found on the street or got the cat from a friend who was no longer able to care for it.

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Figure 17: ATOMIC inferences for the event “PersonX adopted a cat”.

How do we provide machines with large-scale, contextualized commonsense knowledge?

The solution is to leverage manually curated commonsense knowledge resources, such as ConceptNet and ATOMIC, to train a model that can dynamically produce such knowledge for a given context. Commonsense knowledge resources are typically sparse, making training a knowledge base completion model to extend the resource less efficient. Pre-trained language models and their inherent knowledge come in handy here. Language models (such as GPT) implicitly represent knowledge, so you can re-train them on completing knowledge base assertions (e.g. from ATOMIC) to teach them the structure of knowledge. This is what COMET (COMmonsEnse Transformers) does, as illustrated in Figure 18.

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Figure 18: Illustration of the training process of COMET: The language model is fine-tuned to predict the “tail entity” (e.g. inference in ATOMIC) given the “head entity” and the relation. Image credit: Antoine Bosselut.

COMET is capable of dynamically generating inferences for any context. For example, if we modify the context from ATOMIC to " David adopted his sister’s cat because they found out her husband was allergic. ", which for obvious reasons does not appear in ATOMIC, COMET no longer predicts that PersonX (David) had to go to the shelter, but instead that he, for example, needed to find out about it.

COMET has been used successfully in various downstream tasks requiring commonsense knowledge. Models trained on ATOMIC or on ConceptNet are available, and the demo for both ATOMIC and COMET can be found here. There is also a Visual COMET that can generate inferences from images.