“ I ’m Not Mad”: Commonsense Implications of Negation and Contradiction (2021)

Abstract: Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., “I’m mad at you”), humans can reason about the varying shades of contradictory statements ranging from straightforward negations (“I’m not mad at you”) to commonsense contradictions (“I’m happy”). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in interesting and nontrivial ways. For example, while “I’m mad” implies “I’m unhappy about something,” negating the premise 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 ANION, 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.

## introduction humans reason about underlying causes and effects of events described in text. for example, in figure 1 , the event "x wears a mask" is associated with many causal inferences such as "x is seen as responsible," or "others get protected." hypothesizing and reasoning about commonsense inferences is used for understanding complex situations encountered in everyday life @xcite @xcite . this ability eludes ai systems, and has motivated the design of a wealth of figure 1 : commonsense inferences for the event "x wears a mask," its logical negation and commonsense contradiction events, and their associated inferences. however, reasoning about negated observations remains a challenge @xcite . while negation is often considered a poorer form of meaning than affirmation foot_0 @xcite , negated statements can still imply expressive commonsense inferences. in figure 1 , the negated event "x doesn't wear a mask," is connected to rich commonsense inferences, despite describing the absence of action. however, negated observations are rarely found in commonsense knowledge resources. for example, negated examples make up only ∼3% of examples in the conceptnet knowledge graph @xcite . this scarcity poses downstream issues for systems that must understand negated situations. commonsense knowledge models @xcite trained on resources of largely affirmative instances struggle particularly with negation examples. their ability to hypothesize inferences for negated events is 35% lower than for affirmative events ( §4.2). furthermore, since negated statements are asymmetrically mentioned in text compared to affirmative statements @xcite , large-scale pretraining does not implicitly learn negation scoping @xcite . as a result, when presented with negated concepts, pretrained neural language models (plms) often exhibit the same associations as affirmative statements @xcite . motivated by these observations, our work focuses on improving the ability of knowledge models to make commonsense inferences about events that convey denial, rejection or contradiction of actions. we define our contributions as follows. first, we crowdsource a new large scale resource, array of commonsense inferences for oppositions and negations (anion), which contains inferences for different types of negated events.this new resource can be used to train knowledge models on commonsense inferences associated with the absence of actions. second, we propose a new class of negation discriminators that can be applied to generated commonsense inferences. these discriminators partition inferences based on logical consistency, thereby mitigating the effects of common affirmative associations that violate negation constraints. discriminators are trained using contrastive samples from paired affirmative and negated events in anion. finally, we conduct an empirical study of both of these techniques and show that using training-and discriminator-based approaches for modeling negation cuts the performance difference between affirmative and negated events by 73% -85% depending on the negation variety. despite their diversity, however, negated language expressions are much less likely to appear in text than affirmative statements @xcite . consequently, plms, which rely on largescale textual corpora as training data, are prone to decreased performance when confronted with negated constructions. in machine translation, for example, the presence of negation may heavily affect the quality of produced translations @xcite . in factual knowledge understanding tasks, plms memorize positive and negative sentences seen during training, but generalize more poorly to unseen negated instances @xcite . negation in commonsense reasoning understanding negation and oppositional expressions is critical for reasoning about commonsense knowledge, particularly in counterfactual scenarios @xcite . however, negation is rarely explicitly modeled in nlp studies on commonsense reasoning. as a result, in many nlp tasks, these models experience a performance drop when presented with examples exhibiting negated characteristics. as a case study, the atomic @xcite ) knowledge graph encodes social commonsense knowledge about event pre-conditions, event postconditions, and static attributes in the form of natural language if-then rules. however, despite the fact that atomic provides a rich set of seed events, it comprises an unbalanced set of affirmative events (97.9%) and negated events (2.1%). as a result, when systems link to atomic to retrieve relevant social commonsense inferences, they are likely to recover inferences of affirmative events even when searching for negated instances. furthermore, knowledge models that use this resource (e.g., comet; @xcite are unlikely to learn implicit differences between inferences of affirmative and negated events. when given negated events, these models often produce associations of counterpart affirmative events. for example, for ## anion: commonsense inferences of oppositions and negations to provide a rich resource of commonsense inferences for opposition and negation events, we design anion. using the same schema as the atomic knowledge graph @xcite , we initialize 22,483 negated forms paired to original atomic events and crowdsource 627,042 new inferences for these negated events. consistent with atomic, anion is constructed using english formulations of events and inferences. we briefly recap atomic and describe the construction of anion below. ## knowledge models of negated events anion can be used as training data for commonsense models to make inferences about negated events. here, we recap comet @xcite , a commonsense knowledge model, and evaluate how training knowledge models on anion affects their ability to hypothesize commonsense knowledge for negated and oppositional events. ## discriminating inconsistent inferences while training on examples of negated events helps knowledge models generate commonsense inferences for these event types, there is still a large gap compared to their performance on affirmative events. to address this discrepancy, we introduce a discriminator-based approach for distinguishing inconsistent inferences of negated events. our inference discriminator learns to identify plausible and invalid inferences of events by learning from contrastive samples from atomic and anion. ## discussion are learning-based and discriminator-based approaches complementary? we apply our discriminators to the generations of the comet model trained on anion. in can discriminators be used to more aggressively generate inferences? while applying discriminators to generated inferences yields a valid subset with higher accuracy, we are left with fewer correct inferences in total. thus, we investigate the efficiency of using discriminators to expand the number of inferences generated. we decode inferences from comet with beam size 25, and then apply the discriminator to this larger candidate set. ## conclusion we present the first comprehensive study on commonsense implications of negations and contradictions. to expand commonsense resources for the challenge of negation modeling, we introduce an-ion, a large scale commonsense knowledge graph for negated and contradicted events. we use an-ion to train commonsense knowledge models and demonstrate that it effectively enriches machine commonsense inference capabilities around negation. lastly, we propose a negation discriminator capable of identifying logical flaws in commonsense inferences. by combining the model trained on anion with the negation discriminator, we achieve a further performance boost.