# Conversational Semantic Parsing using Dynamic Context Graphs Source: [Conversational Semantic Parsing using Dynamic Context Graphs]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:05:39.468Z Authors: Parag Jain, Mirella Lapata Published: 2023-05-04T16:04:41.000Z ## Abstract In this paper we consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms (e.g., Sparql) in the context of the conversational history. Our key idea is to represent information about an utterance and its context via a subgraph which is created dynamically, i.e., the number of nodes varies per utterance. Rather than treating the subgraph as a sequence, we exploit its underlying structure and encode it with a graph neural network which further allows us to represent a large number of (unseen) nodes. Experimental results show that dynamic context modeling is superior to static approaches, delivering performance improvements across the board (i.e., for simple and complex questions). Our results further confirm that modeling the structure of context is better at processing discourse information, (i.e., at handling ellipsis and resolving coreference) and longer interactions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Knowledge Base Question Answering on SPICE (test)](): Knowledge Base Question Answering; SPICE (test); m-F1 (Logical Reasoning): 54.21. ## Original sources and implementation [Original paper]() [PDF]()