- Maarten Sap. 2018. Exploring string-based “almost joins” for commonsense event intent and reaction annotations. Abstract: Commonsense inference motivation Understanding a narrative often requires commonsense reasoning about the potential mental states of people in relation to events. For example, if “Alex is dragging his feet at work”, pragmatic implications about Alex’s intent are that “Alex wants to avoid doing things” (Figure 1). We can also infer that Alex’s emotional reaction might be “lazy” or “bored”. Furthermore, while not explicitly mentioned in the given event description, we can nonetheless infer that people other than Alex are affected by Alex’s action, and these people are likely to be “frustrated” or “impatient”. This type of pragmatic inference can potentially be useful for a wide range of NLP applications that require accurate anticipation of peoples’ intents and emotional reactions even when they are not stated directly. For example, an ideal dialogue system should react in empathetic ways by reasoning about the human user’s mental state based on the events the user has experienced, without the user explicitly stating how they’re feeling. Similarly, advertisement systems on social media should be able to reason about the emotional reactions of people after events like mass shootings, and remove ads for guns which might increase social distress [Goel and Isaac, 2016]. Also, pragmatic inference is a necessary step toward automatic narrative understanding and generation [Tomai and Forbus, 2010, Ding and Riloff, 2016, Ding et al., 2017]. However, this type of social commonsense reasoning goes far beyond the widely studied entailment tasks [Bowman et al., 2015, Dagan et al., 2006], and thus falls outside the scope of existing benchmarks. In this project, we introduce a new corpus that supports commonsense inference on events with a specific focus on modeling stereotypical intents
