Conversational Semantic Parsing using Dynamic Context Graphs

• URL: http://arxiv.org/abs/2305.06164v2
• Date: Thu, 7 Dec 2023 15:56:21 GMT
• Title: Conversational Semantic Parsing using Dynamic Context Graphs
• Authors: Parag Jain and Mirella Lapata
• Abstract summary: 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.
• Score: 68.72121830563906
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Integrating Large Language Models with Graph-based Reasoning for Conversational Question Answering [58.17090503446995]
  We focus on a conversational question answering task which combines the challenges of understanding questions in context and reasoning over evidence gathered from heterogeneous sources like text, knowledge graphs, tables, and infoboxes. Our method utilizes a graph structured representation to aggregate information about a question and its context.
  arXiv  Detail & Related papers  (2024-06-14T13:28:03Z)
• Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs [6.869834883252353]
  This paper evaluates the performance of large language models that have not been explicitly pre-trained on this task. Our results demonstrate that large language models are capable of generating graph queries from dialogues.
  arXiv  Detail & Related papers  (2024-01-03T12:28:33Z)
• Semantic Parsing for Question Answering over Knowledge Graphs [3.10647754288788]
  We introduce a novel method with graph-to-segment mapping for question answering over knowledge graphs. This method centers on semantic parsing, a key approach for interpreting these utterances. Our framework employs a combination of rule-based and neural-based techniques to parse and construct semantic segment sequences.
  arXiv  Detail & Related papers  (2023-12-01T20:45:06Z)
• Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networks [8.334427140256606]
  We try three different methods for topic-focus articulation (TFA) employing graph neural models for a meaning-to-text generation task. We propose a novel encoding strategy about node aggregation in graph neural models, which instead of traditional encoding by aggregating adjacent node information, learns node representations by using depth-first search.
  arXiv  Detail & Related papers  (2023-10-03T13:51:01Z)
• Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation [23.54754465832362]
  In conventional graph neural networks (GNNs) message passing on a graph is independent from text. This training regime leads to a semantic gap between graph knowledge and text. We propose a novel framework for knowledge graph enhanced dialogue generation.
  arXiv  Detail & Related papers  (2023-06-28T13:21:00Z)
• Scene Graph Modification as Incremental Structure Expanding [61.84291817776118]
  We focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query. We frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE) We construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets.
  arXiv  Detail & Related papers  (2022-09-15T16:26:14Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• Iterative Context-Aware Graph Inference for Visual Dialog [126.016187323249]
  We propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations.
  arXiv  Detail & Related papers  (2020-04-05T13:09:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.