Logical Story Representations via F rame N et + Semantic Parsing (2022)

Abstract: We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events. These schematic representations of the frame events, which we call Episodic Logic (EL) schemas, abstract constants to variables, preserving their types and relationships to other individuals in the same text. Due to the temporal semantics of the chosen logical formalism, all identified schemas in a text are also assigned temporally bound “episodes” and related to one another in time. The semantic role information from the FrameNet frames is also incorporated into the schema’s type constraints. We describe an implementation of this method using a neural FrameNet parser, and discuss the approach’s possible applications to question answering and open-domain event schema learning.

## introduction story understanding requires deep, non-textual representations of textual information. the human brain, neural language models, and formal logic engines all transduce textual input into some other format in order to perform semantic tasks on that input. while formal logical representations of language admit more reliable and explainable inference procedures on text than, for example, the vector representations used by transformers, they suffer from characteristic brittleness when attempting to parse the true logical meaning of text: paraphrases and idioms stymie the logical capture of true semantics at best, and actively lead to incorrect understanding at worst. the framenet project @xcite attempts to provide a taxonomy of event "frames" (sometimes also called "schemas" or "scripts"), including their actors and objects, that one might observe in the real world, and thus in texts discussing the real world. these frames are not tied to any one means of expression: many different constructions, epi-schema ((?x_c (composite-schema.pr ?x_d)) ** ?e) :roles !r1 (?x_a friend.n) !r2 (?x_a (pertain-to ?x_b)) !r3 (?x_b agent.n) !r4 (?x_c mom.n) !r5 (?x_c (pertain-to ?x_b)) !r6 (?x_c motion-theme.n) !r7 (?x_c ingestion-ingestor.n) !r8 (?x_d house.n) !r9 (?x_d (pertain-to ?x_a)) !r10 (?x_d motion-goal.n) !r11 (?x_e food.n) !r12 (?x_e ingestion-ingestibles.n) :steps ?e1 (?x_c motion-go.1.v ?x_d) ?e2 (?x_c ingestion-eat.2.v ?x_e) :episode-relations !w2 (?e1 before ?e2) figure 1 : an example of an episodic logic schema representing the story "jenny's mom went to her friend's house. she ate food there." constants in this story, such as "jenny", have been abstracted to variable names, creating a general schema form of the story, but the original story constants may be re-bound to these variables at any time. noun predicates taken from single story tokens, e.g. friend.n, are color-coded with their variables. noun and verb predicates obtained from framenet matches are underlined, and prefixed with the name of the framenet frame before the hyphen. additional information on the syntax and semantics of the schema is given by @xcite . coreference resolver elf semantic parser framenet span finding (lome) dependency parser token index alignment "jenny's mom went to jenny's friend's house." (mom.sk pertain-to jenny) (friend.sk pertain-to jenny) (house.sk pertain-to friend.sk) (mom.sk ((adv-a (to.p house.sk)) go.v) motion "went" theme "jenny's mom" goal "her friend's house" motion "went" -> go.v theme "mom" -> mom.sk goal "house" -> house.sk "jenny's mom went to her friend's house." figure 2 : the architecture of the system. raw story text is fed along two tracks: the logical-semantic parsing track, shown along the top, and the framenet parsing track, shown along the bottom. the framenet text spans are reduced to direct object tokens and correlated with logical individuals in the elf parse via token index matching. in this paper, we present a means of producing expressive, semantically manipulable, formal logical "schema" representations of stories using a state-of-the-art framenet parsing system, lome @xcite , as a jumping-off point. by augmenting framenet parses with logical semantic representations of the text, we obtain schema-like story representations that mitigate both the brittleness inherent to literal semantic parsing and the difficulty of manipulation inherent to natural language frames. we also discuss the potential application of these representations to the task of automatically acquiring event schema knowledge from natural text corpora. ## semantic representation the semantic representation we provide is based on episodic logic (el) @xcite , a formal logical representation of language that enables efficient inference while maintaining a surface resemblance to the english language. one feature of el that is well suited to story representation is its characterizing operator, ** , which relates an episodic logic formula (elf) to an episode. informally, (ϕ ** e) means that e is "an episode of" some formula ϕ, e.g., in ((?x_c motion-go.1.v ?x_d) ** ?e1), ?e1 is an episode of ?x_c going to ?x_d (cf. the first step in figure 1 ; in schemas the ** operator is left implicit). these episodes, characterized by formulas derived from sentences, have temporal bounds, and can be related to each other in time using relations derived from the allen interval algebra @xcite . episodes are first-class individuals in episodic logic, and may be used as arguments to predicates, such as in the temporal relation formula (e1 before e2). elfs, like those seen in the schema in figure 1 , often have predicates derived from nouns or verbs. for example, the first role condition in figure 1 , the schema elf (?x_a friend.n), asserts that the variable ?x_a satisfies the predicate friend.n (and, as stated in the next role condition, ?x_a "pertains to" ?x_b, i.e. ?x_a is a friend of jenny). the first step of the same schema, the elf (?x_c motion-go.1.v ?x_d), can be read as a subject-verb-object verb phrase, where the arguments to the verb predicate, motion-go.1.v, are the variables ?x_c and ?x_d. ## architecture our system's architecture, illustrated in figure 2 , is divided into two main information pipelines: the el track, responsible for semantic parsing, and the framenet track, responsible for frame identification and span selection. the information from both of these pipelines is unified into a final schematic representation at the end using token indices from the input text. ## discussion the goal of our representation, and of semantic story representations in general, is to enable a variety of reasoning tasks. as the quality of the frames identified by lome has already been evaluated by @xcite , we do not re-evaluate quality after transducing those frames into el schemas. below, we discuss two interesting potential applications of this representation: question answering and event schema acquisition. ## conclusion we have presented a system for obtaining rich, formal logic-based, schema-like representations of stories from text by combining the frame identification power of lome and framenet with the semantic representation power of episodic logic schemas. we showed that these representations normalize language into propositions based on semantic frames; model type, relational, and temporal constraints; and allow for hierarchical nesting of situations. finally, we discussed their potential application, in future work, to tasks that neither framenet nor el parsing alone is trivially capable of, such as paraphrase-resistant question answering, information retrieval, and automatic acquisition of event schemas from text, to which this system has already been applied.