Analogous Process Structure Induction for Sub-event Sequence Prediction (2020)

Abstract: Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories. Thus, knowledge about a known process such as “buying a car” can be used in the context of a new but analogous process such as “buying a house”. Nevertheless, most event understanding work in NLP is still at the ground level and does not consider abstraction. In this paper, we propose an Analogous Process Structure Induction (APSI) framework, which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub-event sequence of previously unseen open-domain processes. As our experiments and analysis indicate, APSI supports the generation of meaningful sub-event sequences for unseen processes and can help predict missing events.

## introduction understanding events has long been a challenging task in nlp, to which many efforts have been devoted by the community. however, most existing works are focusing on procedural (or horizontal) event prediction tasks. examples include predicting the next event given an observed event sequence @xcite and identifying the effect of a biological process (i.e., a sequence of events) on involved entities @xcite . these tasks mostly focus on predicting related events in a procedure based on their statistical correlations in previously observed text. as a result, understanding the meaning of an event might not be crucial for these horizontal tasks. for example, simply selecting the most frequently cooccurring event can offer acceptable performance on the event prediction task @xcite . computational and cognitive studies @xcite suggest that inducing and utilizing the hierarchical structure foot_0 of events is a crucial component of how humans understand new events and can help many aforementioned horizontal event prediction tasks. consider the example in figure 1 . assume that one has never bought a house, but is familiar with how to "buy a car" and "rent a house"; referring to analogous steps in these two relevant processes would still provide guidance for the target process of "buy a house". motivated by this hypothesis, our work proposes to directly evaluate a model's event understanding ability. we define this as the figure 2 : demonstration of the proposed apsi framework. given a target process p , we first decompose its semantics into two dimensions (i.e., predicate and argument) by grouping processes that share a predicate or an argument. for each such group of processes, we then leverage the observed process graphs g to generate an abstract and probabilistic representation for their sub-event sequences. in the last step, we merge them with an instantiation module to produce the sub-event sequence of p . to address this problem, we propose an analogous process structure induction (apsi) framework. given a new process definition (e.g., 'buy a house'), we first decompose it into two dimensions: predicate and argument. for each of these, we collect a group of processes that share the same predicate (i.e., 'buy-arg') or same argument (i.e., 'pre-house'), and then induce an abstract and probabilistic sub-event representation for each group. our underlying assumption is that processes that share the same predicate or argument could be analogous to each other, and thus could share similar sub-event structures. finally, we merge these two abstract representations, using an instantiation module, to predict the subevent structure of the target process. by doing so, we only need a small number of analogous processes (as we show, 20, on average) to generate unseen sub-events for the target process. intrinsic and extrinsic evaluations show that apsi outperforms all baseline methods and can generate meaningful sub-event sequences for unseen processes, which are proven to be helpful for predicting missing events. the rest of the paper is organized as follows. section 2 introduces the analogous process structure induction (apsi) framework. section 3 describes our intrinsic and extrinsic evaluation, demonstrating the effectiveness of apsi and the quality of the induced process knowledge. we discuss related works in section 4 and conclude this paper with section 5. ## the apsi framework figure 2 illustrates the details of the proposed apsi framework. given an unseen process p , a target sub-event sequence length k, and a set of observed process graphs g, the task is to predict a we assume that each process p is described as a combination of a predicate and an argument (e.g., 'buy+house') and each sub-event @xmath0. in apsi, we decompose the target process into two dimensions (i.e., predicate and argument). for each target process, we collect a group of observed process graphs that share either the predicate or the argument with the target process; we assume that processes in these groups have sufficient information for predicting the structure of the target process. we then leverage an event conceptualization module to induce an abstract representation of each process group. finally, we merge the two abstract, probabilistic representations and instantiate it to generate a ground sub-event sequence as the final prediction. detailed descriptions of apsi components are introduced as follows. ## evaluation in this section, we conduct intrinsic and extrinsic evaluations to show that apsi can generate meaningful sub-event sequences for unseen processes, which can help predict the missing events. ## conclusion in this paper, we try to understand events vertically by viewing them as processes and predicting their sub-event sequences. our apsi framework is motivated by the notion of analogous processes, and attempts to transfer knowledge from (a very small number of) familiar processes to a new one. the intrinsic evaluation demonstrates the effectiveness of apsi and the quality of the predicted sub-event sequences. moreover, the extrinsic evaluation shows that, even with a naive ap-plication method, the process knowledge can help better predict missing events.