Semantic Vector Spaces for Broadening Consideration of Consequences
Reasoning systems with too simple a model of the world and human intent are unable to consider potential negative side effects of their actions and modify their plans to avoid them (e.g., avoiding potential errors). However, hand-encoding the enormous and subtle body of facts that constitutes common sense into a knowledge base has proved too difficult despite decades of work. Distributed semantic vector spaces learned from large text corpora, on the other hand, can learn representations that capture shades of meaning of common-sense concepts and perform analogical and associational reasoning in ways that knowledge bases are too rigid to perform, by encoding concepts and the relations between them as geometric structures. These have, however, the disadvantage of being unreliable, poorly understood, and biased in their view of the world by the source material. This chapter will discuss how these approaches may be combined in a way that combines the best properties of each for understanding the world and human intentions in a richer way.
Code (0)
등록된 구현이 없습니다.
Tasks
Common Sense ReasoningSimilar Papers 제목 키워드 기반
Discrete Semantic States and Hamiltonian Dynamics in LLM Embedding Spaces
We investigate the structure of Large Language Model (LLM) embedding spaces using mathematical concepts, particularly linear algebra and the Hamiltonian formalism, drawing inspiration from analogies with quantum mechanic…
Reducing DNN Properties to Enable Falsification with Adversarial Attacks
Deep Neural Networks (DNN) are increasingly being deployed in safety-critical domains, from autonomous vehicles to medical devices, where the consequences of errors demand techniques that can provide stronger guarantees …
Adversarial AttackAutonomous VehiclesSemantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-l…
Dialogue State TrackingSemantic SimilaritySemantic Textual SimilaritySemantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-l…
Dialogue State TrackingRepresentation LearningSemantic SimilaritySemantic Textual SimilarityVector spaces as Kripke frames
In recent years, the compositional distributional approach in computational linguistics has opened the way for an integration of the \emph{lexical} aspects of meaning into Lambek's type-logical grammar program. This appr…