Learning General Event Schemas with Episodic Logic (2021)

Abstract: We present a system for learning generalized, stereotypical patterns of events—or “schemas”—from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a “head start” set of protoschemas— schemas that a 1- or 2-year-old child would likely know—we can obtain useful, general world knowledge with very few story examples—often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available.

## introduction we present a novel approach to learning rich, symbolic event schemas from natural language texts. while most modern approaches to automated script learning (e.g. @xcite @xcite ) learn linear sequences of simple tuple representations of events, our schema representation allows for typed and interrelated participating entities; multiple temporally related subevents; specification of goals, preconditions, and postconditions; and nesting of subschemas as steps in another schema. we mitigate the "brittleness" of past symbolic approaches (e.g., @xcite and ipp @xcite ) by parsing stories into episodic logical form (elf) @xcite , a logical representation that closely resembles natural english, but allows for complex event representation and powerful inference procedures. as @xcite argue, episodic logic facilitates "natural logic-like inference while also providing greater generality". el, and its underspecified variant ulf, facilitate nloglike inferences using a combination of lexical and semantic knowledge @xcite . because most nouns and verbs are preserved as predicates in elfs, we also utilize existing lexical resources, like wordnet's hypernym hierarchy for generalizing schema predicates (e.g. dog.n and elephant.n to placental mammal.n), and semantic word embeddings for retrieving relevant schema candidates for a story from a large number of known schemas. we also bypass the need for large amounts of data by giving the system a "head start" in the form of a relatively small number of initial schemas targeting the commonsense knowledge of a very young child, from which more complex schemas can be learned and composed. these "protoschemas" describe basic action types-e.g., eating, searching, moving from place to place, transferring possession of objects-at a very general level, along with their underlying motivations and pre-and postconditions. more complex schemase.g., "a monkey climbs a tree, gets a coconut, and eats the coconut"-can be composed by "chaining" these simpler ones together after matching them to a story. from a corpus of several hundred short children's stories, we have acquired dozens of schema matches, generalized them into new schemas, automatically composed some of them into more complex schemas, and used those generalized schemas to make predictions on unseen stories with only partial information. ## episodic logic our schema representation is based on episodic logic (el) @xcite , a formal knowledge representation with semantic types and operators common to many natural languages. el uses first-order quantification, but has type-shifting and reification operators to map predicate and sentence intensions to domain individuals, allowing it to represent higher-order propositions. el is a good fit for schemas in part because of its characterizing operator ** , which relates an el formula to a situational argument, an "episode" that it characterizes. for example, the el formula ((i.pro eat.v (k steak.n)) ** e1) says that e1 is a (possibly repetitive, habitual) episode of me eating steak foot_0 . episodes can have multiple formulas "true in" them, where these formulas characterize subepisodes with limited temporal bounds. this makes them ideal for representing entire schemas, which are "packages" of formulas all true together within some span of time. ## schema representation in this section, we will describe our schema representation. although sequential and causally connected events play a large role in our schemas, our schema language is differentiated from causal representations such as @xcite and sequential script representations such as @xcite ) by the expressiveness and interconnectedness of its constituent logical formulas. the language is designed to model the schema's steps, the roles (types) of participating entities, and the motivating goals, preconditions, and postconditions of the schema as a whole. an example schema our system has learned can be seen in figure 1 . the el formulas specifying the semantic contents of a schema organized into sections; we describe the sections below. ## schema learning in this section, we describe how our system learns new schemas from natural language stories. we describe our story parsing process, the process of matching parsed stories to schemas, how schema matches can be generalized to create new schemas, and how partial schema matches can be used to predict events in similar stories with missing details. ## results using 511 simple stories taken from a children's first reader @xcite and the rocstories corpus @xcite , and 13 protoschemas 4 , we obtained 665 schemas, with a mean score of -0.899, a median score of 0.292, a minimum score of -19.304, and a maximum score of 4.5 according to the scoring metric in section 4.3.1. after filtering out the 314 negative-scoring schemas, we obtained 314 "specified" schemas, including six multi-step schemas, examples of which can be found in figure 1 and figure 2 . 4 these 13 protoschemas, including traveling from place to place, eating food, taking possession of an object, and searching for something, were selected to cover a large number of sentences in a "development set" of 50 held-out stories from our corpus of 561 stories; 511 were used in the test set. we intend to eventually construct dozens to hundreds of initial protoschemas. figure 2 : an example of a multi-step schema learned by our system from protoschema matches to a story about a monkey climbing a tree to get and eat a cocoanut. the schema in figure 2 inferred, given the sentences "simeon can climb the tree" and "he gets the cocoanuts for his mother", that simeon was a monkey, that he got the cocoanuts in the tree, and that he later ate the cocoanuts. the schema in figure 1 inferred, given the sentences "the bees like it", "they find sweet nectar in the clover flowers", and "it grows in the fields", that the bees went to the fields to find the nectar. these predictions about unseen stories are reasonable and fill in details absent in the stories themselves. ## future work the schemas learned and predictions generated by the system with only 13 protoschemas are encouraging; we've obtained many simple schemas, like "person sits in a chair" or "dogs run around outside", as well as complex, multi-step schemas used for predictions like the ones in section 5. because complex schemas are made by stringing together protoschema matches, we plan to develop more protoschemas-possibly dozens to hundreds-to more fully cover the general knowledge of a twoyear-old child. with those protoschemas as a base, we expect to generate many more useful, multi-step schemas, use them to generate predictions about stories, and have human judges evaluate those predictions.