Modeling Complex Event Scenarios via Simple Entity-focused Questions (2023)

Abstract: Event scenarios are often complex and involve multiple event sequences connected through different entity participants. Exploring such complex scenarios requires an ability to branch through different sequences, something that is difficult to achieve with standard event language modeling. To address this, we propose a question-guided generation framework that models events in complex scenarios as answers to questions about participants. At any step in the generation process, the framework uses the previously-generated events as context, but generates the next event as an answer to one of three questions: what else a participant did, what else happened to a participant, or what else happened. The participants and the questions themselves can be sampled or be provided as input from a user, allowing for controllable exploration. Our empirical evaluation shows that this question-guided generation provides better coverage of participants, diverse events within a domain, comparable perplexities for modeling event sequences, and more effective control for interactive schema generation.

## introduction event scripts @xcite , also known as event schemas, describe a sequence of events in a particular context. representing and modeling such schemas is central to applications in ai such as question answering, discourse understanding, and information extraction @xcite . early work used hand-crafted event schemas as a starting point @xcite , but modern techniques attempt to extract these at a large scale from unlabeled data @xcite @xcite . event language models can also be used to approximate schematic knowledge via event sequences. they can be trained to generate a sequence of events with their participating roles describing a real-life scenario. however, these scenarios can often be complex and don't always fit as simple sequences @xcite . for example, suppose we have the following event: police arrested suspect on several charges, with police, suspect and charges as entities. the scenario can be described in multiple ways depending on which entities we want to focus on and what roles they play in the subsequent events. we may be interested in knowing what the police did, what the suspect did or what happened to the charges, each of which can be explored as its own sequence, yielding many interconnected sequences of events in the scenario. an event language model, however, will simply generate events conditioned on the previous events in the discourse, with no direct mechanism to guide generation towards areas of interest within the scenario. as a result, with standard decoding strategies we often end up with a sequence of events that might be relevant to the scenario, but not necessarily cover the broad set of diverse paths in the scenario; we would only know the fate of certain entities if the system samples events that included them. this lack of control will make it difficult to use the system, as one must keep sampling events until it just happens to produce events with roles one is interested in. we propose a simple modification in which event language models are trained to also condition on the entities and the roles they play in a scenario through a set of simple questions, as illustrated in figure 1 . given the same example event and its entities, one can then explore the scenario through various paths by asking the system to generate the events with their desired entities and roles. such a question-guided model can be used in interactive settings to model and construct diverse paths that cover various aspects of complex scenarios. a key challenge, however, is in creating the necessary training data at scale. we show that we can repurpose standard event sequences to create training data for question-guided models: we take a partial sequence of events as context and derive a role-based question involving an entity for which a future event in the sequence can be an answer. this allows for creating large scale training instances that are (context, question, answer) triples, which then allows us to train models that can better respond to user control in the form of questions. our analysis shows that question-guided event language models can generate sequences with more diversity and comparable quality as an event language model. our human evaluation of the model in an interactive setting, shows that the controllability of the question-guided model allows for generation of sequences that lead to better quality, broader-coverage schemas with fewer interactions. this interactive evaluation is a step toward constructing schematic/common-sense knowledge for analyzing events, an application in the intelligence analysis community. in summary, this paper makes the following contributions; (i) it argues the need for control in event language models to explore complex scenarios; (ii) it provides a simple yet effective way for training event language models that can be guided to explore different aspects of complex scenarios; (iii) it provides empirical evidence showing improved control and utility via automatic and manual evaluations. early works on scripts considered them as structured representations of events and their participants with the causal relationships between them @xcite . however, the manual construction of scripts is too time-consuming and does not scale, so the scripts could only focus on specific domains of interest and have not been used more broadly. event schemas can also be induced automatically from text using statistical techniques in an unsupervised fashion @xcite @xcite . these models are easily interpretable but fail to capture long-distance complex relationships between events. event language modeling is a type of schema induction via language modeling techniques @xcite . given a sequence of events, the event language model predicts the probability of the next event @xcite . framing schema learning as a language modeling problem with various ways to represent events, including word sequences annotated with predicate-argument structure @xcite , openie tuples @xcite or compositional embeddings @xcite , is another direction towards realizing large-scale schema libraries. graph schema induction methods @xcite model different relations between entities and their arguments to capture the multidimensionality of scenarios. reasoning about complex relations between events requires going beyond the single dimension of event cooccurrence and capturing different types of semantic relations between events such as causal, counterfactual, etc. @xcite . our approach uses the standard language models and provides them with the guidance to also produce different aspects of a scenario via simple control codes. ## question-guided event language modeling event language models aim at predicting the probability of an event given a set of events via a conditional probability distribution. formally, these models try to find an event ê by maximizing the probability of a function parameterized by a model over a given context including a set of events: ## evaluation we assess the utility of the question-guided event language modeling in terms of four aspects: (i) diversity: are they able to generate diverse sets of events that relate to a scenario? (ii) control: do they generate events involving the specific entities in desired roles? (iii) sequence modeling ability: how well can it predict observed events?, (iv) interactive utility: do users generate better sequences when using the model to collect events that fit a scenario? ## conclusion controlling event language models to generate events with respect to the participants is not trivial. we propose a simple yet effective questionguided approach that learns to generate events by not only conditioning on the events but also on specific entity-based questions of interest. our empirical analysis shows that this approach can be used to generate more diverse sequences with better coverage and controllability allowing for better modeling of complex scenarios.