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Register a new userIm Not Mad: Commonsense Implications of Negation and Contradiction
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Added by
Liwei Jiang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., Im mad at you), humans can reason about the varying shades of contradictory statements ranging from straightforward negations (Im not mad at you) to commonsense contradictions (Im happy). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in nontrivial ways. For example, while Im mad implies Im unhappy about something, negating the premise (i.e., Im not mad) does not necessarily negate the corresponding commonsense implications. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION1, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.
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With the increasing empirical success of distributional models of compositional semantics, it is timely to consider the types of textual logic that such models are capable of capturing. In this paper, we address shortcomings in the ability of current models to capture logical operations such as negation. As a solution we propose a tripartite formulation for a continuous vector space representation of semantics and subsequently use this representation to develop a formal compositional notion of negation within such models.
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