Think about it! Improving defeasible reasoning by first modeling the question scenario. (2021)

Abstract: Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a “mental model” of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our system, CURIOUS, achieves a new state-of-the-art on three different defeasible reasoning datasets. This result is significant as it illustrates that performance can be improved by guiding a system to “think about” a question and explicitly model the scenario, rather than answering reflexively.

## introduction defeasible inference is a mode of reasoning where additional information can modify conclusions @xcite . here we consider the specific formulation and challenge in @xcite : given that some premise p plausibly implies a hypothesis h, does new information that the situation is s weaken or strengthen the conclusion h? for example, consider the premise "the drinking glass fell" with a possible implication "the glass broke". new information that "the glass fell on a pillow" here weakens the implication. we borrow ideas from the cognitive science literature that supports defeasible reasoning for humans with an inference graph @xcite . inference graphs formulation in @xcite , which we use in this paper, draws connections between the p, h, and s through mediating 1 data and code located at https://github.com/ madaan/thinkaboutit events. this can be seen as a mental model of the question scenario before answering the question @xcite . this paper asks the natural question: can modeling the question scenario with inference graphs help machines in defeasible reasoning? our approach is as follows. first, given a question, generate an inference graph describing important influences between question elements. then, use that graph as an additional input when answering the defeasible reasoning query. our proposed system, curious, comprises a graph generation module and a graph encoding module to use the generated graph for the query (figure 2 ). to generate inference graphs, we build upon past work that uses a sequence to sequence approach @xcite . however, our analysis revealed that the graphs can often be erroneous, and curious also includes an error correction module to generate higher quality inference graphs. this was important because we found that better graphs are more helpful in the downstream qa task. the generated inference graph is then used for the qa task on three existing defeasible inference datasets from diverse domains, viz., δ-snli (natural language inference) @xcite , δ-social (reasoning about social norms) @xcite , and δ-atomic (commonsense reasoning) @xcite . we show that the way the graph is encoded for input is important. if we simply augment the question with the generated graphs, there are some gains on all datasets. however, the accuracy improves substantially across all datasets with a more judicious encoding of the graph-augmented question that accounts for interactions between the graph nodes. to achieve this, we use the mixture of experts approach @xcite to include a mixture of experts layers during encoding, enabling the ability to attend to specific nodes while capturing their interactions selectively. in summary, our contribution is in drawing on the idea of an inference graph from cognitive science to show benefits in a defeasible inference qa task. using an error correction module in the graph generation process, and a judicious encoding of the graph augmented question, curious achieves a new state-of-the-art over three defeasible datasets. this result is significant also because our work illustrates that guiding a system to "think about" a question before answering can improve performance. ## task we use the defeasible inference task and datasets defined in @xcite , namely given an input @xmath0,h,s), predict the output @xmath1, weakens}, where p, h, and s are sentences describing a premise, hypothesis, and scenario respectively, and y denotes whether s strengthens/weakens the plausible conclusion that h follows from p, as described in section 1. ## approach inspired by past results @xcite that humans found inference graphs useful for defeasible inference, we investigate whether neural models can benefit from envisioning the question scenario using an inference graph before answering a defeasible inference query. inference graphs as inference graphs are central to our work, we give a brief description of their structure next. inference graphs were introduced in philosophy by @xcite to aid defeasible reasoning for humans, and in nlp by @xcite for a counterfactual reasoning task. we interpret the inference graphs as having four kinds of nodes @xcite : i. contextualizers (c-, c+): these nodes set the context around a situation and connect to the p. each node in an influence graph is labeled with an event (a sentence or a phrase). the signsand + capture the nature of the influence event node. concrete examples are present in figures 1 , 4 , and in appendix §d. ## experiments in this section, we empirically investigate if curi-ous can improve defeasible inference by first modeling the question scenario using inference graphs. we also study the reasons for any improvements. datasets our end task performance is measured on the three existing datasets for defeasible inference provided by rudinger et al. (2020): foot_0 δ-atomic, δ-snli, δ-social ( ## explainability in this section, we analyze the explainability of curious model. @xcite note that an explanation should aim towards two complementary goals: i) plausibility: provide an interpretation of system outputs that is convincing for humans, and ii) faithfulness: capture the actual reasoning process of a model. we discuss how our approach takes a step towards addressing these goals. plausibility in a prior work, @xcite show that human annotators selectively picked and chose parts of the graph that explained a model decision and enabled them in improving on the task of defeasible reasoning. we show in §4.3.3, the moe gate values gives insights into the part of the graph (contextualizer, mediator, situation node) that the model leveraged to answer a query. our model thus produces a reasoning chain that is similar to the explanation that humans understand, providing a step towards building inherently plausible models, while also achieving better performance (as humans did after doing the task with explanation). measuring faithfulness w.r.t. graphs since faithfulness is a widely debated term, we restrict its definition to measure faithfulness w.r.t to the reasoning graph. this can be measured by the correlation between the model performance and graph correctness. a high correlation implies that the model uses both the graph and query to generate an answer and thus is faithful to the stated reasoning mechanism (i.e., graphs used to answer a question). our analysis reveals this to be the case: in cases where the model answers incorrectly, 42% of the graphs were entirely correct ( §5). in contrast, when the model answers correctly, 82% of the graphs are correct. in summary, we hope that curious serves as a step towards building reasoning models that are both plausible and faithful. ## related work mental models cognitive science has long promoted mental models -coherent, constructed representations of the world -as central to @xcite @xcite . our work draws on these ideas, using inference graphs to represent the machine's "mental model" of the problem at hand. building the inference graph can be viewed as first asking clarification questions about the context before answering. this is similar to self-talk @xcite but directed towards eliciting chains of influence. injecting commonsense knowledge many prior systems use commonsense knowledge to aid question-answering, e.g., using sentences retrieved from a corpus @xcite , or with knowledge generated from a separate source @xcite ; and injected either as extra sentences fed directly to the model @xcite , via the loss function @xcite , or via attention @xcite . unlike prior work, we use conditional language generation techniques to create graphs that are relevant to answering a question. encoding graph representations several existing methods use graphs as an additional input for commonsense reasoning @xcite @xcite @xcite @xcite . these methods first retrieve a graph relevant to a question using information retrieval techniques and then encode the graph using graph representation techniques like gcn @xcite and graph attention @xcite . different from these works, we use a graph generated from the query for answering the commonsense question. the graphs consumed by these works contain entities grounded in knowledge graphs like conceptnet @xcite , whereas we perform reasoning over event inference graphs where each node describes an event. our best model uses a mixture-of-experts (moe) @xcite model to pool multifaceted input. prior work has shown the effectiveness of using moe for graph classification @xcite , cross-lingual language learning @xcite , and model ensemble learning @xcite . to the best of our knowledge, we are the first to use moe for learning and pooling graph representations for qa task. ## summary and conclusion cognitive science suggests that people form "mental models" of a situation to answer questions about it. drawing on those ideas, we have presented a simple instantiation in which the situational model is an inference graph. different from gcn-based models popular in graph learning, we use mixtureof-experts to pool graph representations. our experiments show that moe-based pooling can be a strong (both in terms of performance and explainability) alternative to gcn for graph-based learning for reasoning tasks. our method establishes a new state-of-the-art on three defeasible reasoning datasets. overall, our method shows that performance can be improved by guiding a system to "think about" a question and explicitly model the scenario, rather than answering reflexively.