Thinking Like a Skeptic: Defeasible Inference in Natural Language (2020)

Abstract: Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin). Though long recognized in classical AI and philosophy, defeasible inference has not been extensively studied in the context of contemporary data-driven research on natural language inference and commonsense reasoning. We introduce Defeasible NLI (abbreviated \delta -NLI), a dataset for defeasible inference in natural language. Defeasible NLI contains extensions to three existing inference datasets covering diverse modes of reasoning: common sense, natural language inference, and social norms. From Defeasible NLI, we develop both a classification and generation task for defeasible inference, and demonstrate that the generation task is much more challenging. Despite lagging human performance, however, generative models trained on this data are capable of writing sentences that weaken or strengthen a specified inference up to 68% of the time.

## introduction commonsense reasoning tasks are frequently formulated in terms of soft inferences: what is likely or plausibly true given some context, rather than (or in addition to) what is necessarily true. given a context such as "the drinking glass fell", it is common sense to infer that what likely happened next is "the drinking glass broke". however, with the addition of new information, this inference may be blocked or weakened. if, for example, we subsequently learn that "the glass fell onto a pile of laundry" or that "the glass was made of durable material", our original expectation that the glass will break is greatly diminished. this pattern of reasoning, in which an initially supported inference may subsequently be weakened or retracted in light two men and a dog are standing among rolling green hills. they are wearing backpacks. one man is using his binoculars. the men are studying a tour map. the men are holding pitchforks. the men are facing their granary. the dog is a sheep dog. the men are farmers. premise: hypothesis: figure 1 : examples from the δ-snli portion of the δ-nli dataset. a neutral premise-hypothesis pair from snli is augmented with three update sentences that weaken the hypothesis (left, red) and three update sentences that strengthen it (right, blue). tasks like the recognizing textual entailment (rte) challenge @xcite or stanford natural language inference (snli) @xcite capture entailment relations between a fixed context (premise) and inference (hypothesis), but do not reveal how these relations may shift in light of new information about the context. similarly, knowledge graphs for commonsense reasoning like atomic @xcite or conceptnet @xcite encode inference rules about generic situations, but do not elaborate on possible exceptions to the applications of those rules. in this work, we introduce defeasible nli (abbreviated δ-nli, pronounced "delta-nli"), a new dataset to study defeasible inference in natural lan- ## background and related work defeasible reasoning is soft inference based on default assumptions to account for unknown facts, for example, "tweety is a bird" entails that "tweety flies", because birds usually fly. such a conclusion is not deductively valid, and might be invalidated by new information such as "tweety is a penguin" @xcite . defeasible reasoning is a type of nonmonotonic logic, as it contrasts the monotonicity property of classical logic, according to which valid inferences cannot be defeated by adding additional information @xcite . defeasible reasoning has been studied in a range of fields from logic, through linguistics and artificial intelligence. ## definition in this paper, we employ a working definition of defeasible inference that may be seen as an outgrowth of prior work. @xcite introduced the following informal definition for the recognizing textual entailment (rte) task: similarly, the task of natural language inference (nli) seeks to determine whether a (onedirectional) entailment relation exists between a premise sentence and a hypothesis sentence @xcite . while the rte and nli tasks treat entailment relations as fixed, in this work we seek to understand how the introduction of new information can dynamically and directionally affect the strength of inference. thus, our working definition of defeasible inference extends the rte and nli task formulations to model the relationship between a premise, hypothesis, and a third update sentence: given premise p, a hypothesis h is defeasible if there exists an update u (consistent with p) such that a human would find h less likely to be true after learning u. specifically, an update u is called a weakener if, given a premise p and hypothesis h, a human would most likely find h less likely to be true after learning u; if they would find h more likely to be true, then we call u a strengthener. by introducing both strengtheners and weakeners, we generalize from defeasibility as a onedirectional phenomenon (weakening only) to study the bi-directional phenomenon. ## data sources we collect strengtheners and weakeners for three different types of data sources that illustrate the generality of the defeasible inference framework. ## data collection our data collection is performed via crowdsourcing ( §5.1) and consists of two steps: update sentence elicitation ( §5.2) and validation ( §5.3). ## defeasible inference tasks we formulate two tasks: a discriminative defeasible inference task ( §6.1) and a generative defeasible inference task ( §6.2). ## analysis of generated updates in order to analyze the quality of the generated updates, we sampled 150 instances from the development set (25 for each combination of task and model), and categorized their top prediction into the following categories, exemplified in dual-purpose updates. in addition, we looked into instances from the development where a single model generated an identical sentence as both a strengthener and weakener (for a given premisehypothesis pair). ideally, such instances should be rare, as a sentence may increase or decrease the likelihood of a hypothesis, but not both. in practice, we found such overlaps to be a very common failure mode. for a given premise-hypothesis input, we measure the frequency with which each model generates an identical sentence across the top five sampled strengtheners and top five sampled weakeners. the percentage of inputs resulting in such overlaps was extremely high for the bart models: 96.53%, 97.53%, and 99.48% for δ-atomic, δ-social, and δ-snli, respectively @xcite . the corresponding rates for the gpt2 models were much lower (although non-negligible): 48.42%, 33.91%, and 33.91%, respectively. is the premise necessary? in the classification task, we observe that models trained without access to the premise perform nearly as well as those trained with access to the full context (premise, hypothesis, update). this raises the interesting question of what role the premise plays in defeasible natural language inference. it is possible that in many cases, the premise is not as crucial as one might expect. recall the classic example of defeasible reasoning: "tweety is a bird" (premise), therefore "tweety flies" (hypothesis), however "tweety is a penguin" (update), and thus tweety does not fly. in this case, it is evident that, while the premise was necessary to originally derive the hypothesis, the update alone is sufficient to conclude the hypothesis no longer holds. 5 in fact, the premise is entailed by the update, and perhaps even discernible from the hypothesis. however, we should not conclude the premise is unnecessary in all cases. in the generative task, removing the premise makes only a slight difference in performance for δ-atomic (∆1.64%) but a substantial difference for δ-snli (∆10.93%) (perhaps due to more specific contexts in snli). because all generative models lag human performance, however, it may simply be a property of current models that they are unable to effectively leverage information from the premise; to match human performance, they may need to leverage this information. for further analysis, we took outputs from the gpt2-xl h-only model on snli and ask human evaluators to assess the outputs under two conditions: (1) annotator observing only the hypothesis, and (2) annotator observing both the premise and hypothesis. in 47.8% of cases, the output was labeled correct in both conditions; 34.1% of cases were labeled incorrect in both conditions. interestingly, in 12.4% of cases, the output was labeled correct in condition (1) and incorrect in condition (2). this finding points to a proportion of cases where the model would need to integrate information from the premise to generate valid strengtheners and weakeners. ## limitations of elicitation to collect the strengthener and weakener sentences in this work, we elicited sentences from crowdsource workers. elicitation as a method of text data collection has a number of known flaws. in particular, (1) annotators may use label-dependent heuristics or strategies to produce sentences that introduce superficial correlations between text features and labels @xcite @xcite ; (2) elicitation may result in repeated responses of salient answers that are a small subset of all possible valid answers @xcite ; and (3) elicited responses may contain implicit judgments or stereotypic associations about gender, race, and age, among others @xcite . to avoid the first issue of annotation artifacts, we focus primarily on the generative task formulation, which is less susceptible to this problem. regarding the second issue of coverage (or recall), we note that in this work we are concerned with whether it is possible for models to generate any correct weakeners or strengtheners in the first place; evaluating their ability to generate more exhaustively is a challenge we leave for future work. to address the third concern, we explicitly ask annotators to avoid such stereotyped associations in their responses. (see supplement for details.) this is an imperfect but expedient solution and for this reason we caution that the collected data is intended at this stage for scientific purposes only. furthermore, we note that the elicited strengtheners and weakeners about social norms are subjective and, often, culturally dependent. this data should therefore be understood as descriptive of social norms (and their inherent subjectivity), rather than prescriptive of them. ## conclusion and future work to the best of our knowledge, this is the first work to attempt merging long-standing ideas in ai about defeasible reasoning with contemporary formulations of natural language inference and commonsense reasoning tasks. we do this by crowdsourc-ing extensions to three existing inference datasets with enriched contexts that exemplify cases in which an inference is strengthened or weakened. from the collected data, we formulate a classification task and a generation task for defeasible inference in natural language. after demonstrating that the classification task is easily solved by stateof-the-art pretrained language models, we focus instead on the generative task of creating strengtheners or weakeners for a given premise-hypothesis pair, which we liken to "thinking like a skeptic." we demonstrate that fine-tuned language models successfully generate good-quality weakeners and strengtheners in 61-68% of cases. machine reasoning about the plausibility of inferences @xcite , let alone plausibility under different circumstances, is considered an unsolved problem and an obstacle to developing machine commonsense @xcite . an inference engine with such capabilities may potentially be useful for various applications that require reassessing conclusions under changing conditions, such as processing legal texts @xcite and mining arguments @xcite . in knowledge base completion, a "closed world" or default assumptions require the ability to defeat such assumptions given the appropriate counter evidence. such ability was built into the cyc inference engine @xcite , but was largely absent from modern knowledge bases. yet, a number of challenges remain for future work. in our qualitative analysis of generated outputs ( §7), we identify a number of systematic error types that future modeling efforts may seek to address. while this work addresses the quality and accuracy of generated outputs, we leave the more challenging task of evaluating the coverage (recall) of those outputs to future work. finally, joint modeling between defeasible inference and related reasoning tasks such as abductive reasoning @xcite and counterfactual reasoning @xcite @xcite is a potentially fruitful line of inquiry.