Enabling Common Sense Reasoning in Natural Language Processing Systems

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Artificial intelligence systems that understand and generate natural language have made great strides in recent years, leading to successful commercial deployments. Nevertheless, there is ample evidence that current natural language processing (NLP) systems fail in systematic and un-human-like ways, despite high levels of average performance. Much of this failure is due to the lack of knowledge about typical causes and effects in the world, as well as a limited ability to reason about them; in other words, they lack common sense knowledge about the world. My research program aims to develop NLP systems that are capable of such common sense reasoning skills, such that they can communicate and collaborate with human users without requiring explicit training for every new task or topic. The key idea of the proposed research is to build an explicit formal structure that connects the textual elements to each other, on the one hand, and also to relevant background information that the system has already built up knowledge about, on the other. On the technical side, we will propose new neural network architectures and models, which are promising because they provide easy and natural methods to transfer knowledge between multiple sources and tasks via network parameter sharing in multi-task learning and transfer learning setups. The objectives of the project will be organized by the type of common sense and world knowledge structure that is required for a particular inference task. The first one is to make use of the extensive structured knowledge base and resource about common entities and event sequences, such as Wikipedia, and ConceptNet. Here, the target NLP applications we aim to improve are entity and event coreference resolution, which involve determining which textual elements point to the same real-world entities and events, respectively. A second objective domain is in reasoning about emotions, social interactions, and the inner mental states of others, which is known in cognitive science as possessing a theory of mind. Being able to reason about the likely reactions of others is a crucial step in generating contextually appropriate responses in conversational agents, and in ensuring coherence in longer generated passages. A third, challenging domain is that of fictional scenarios and situations, in which the typical expectation of the world which might be learned by reading large amounts of web text might be subverted in interesting and predictable ways. A system that is truly capable of common sense reasoning should be able to imagine the potential consequences even in such unlikely scenarios; e.g., that humans living on Mars would need a spacesuit to breathe or to terraform it so that there is oxygen in the atmosphere.