DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs

• URL: http://arxiv.org/abs/2012.14837v1
• Date: Tue, 29 Dec 2020 16:36:49 GMT
• Title: DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs
• Authors: Lasha Abzianidze, Johan Bos, Stephan Oepen
• Abstract summary: The paper describes the procedure of dressing up DRSs as directed labeled graphs to include DRT as a new framework. The conversion procedure was biased towards making the DRT graph framework somewhat similar to other graph-based meaning representation frameworks.
• Score: 4.21235641628176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse. Meaning in DRT is modeled via a Discourse Representation Structure (DRS), a meaning representation with a model-theoretic interpretation, which is usually depicted as nested boxes. In contrast, a directed labeled graph is a common data structure used to encode semantics of natural language texts. The paper describes the procedure of dressing up DRSs as directed labeled graphs to include DRT as a new framework in the 2020 shared task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. Since one of the goals of the shared task is to encourage unified models for several semantic graph frameworks, the conversion procedure was biased towards making the DRT graph framework somewhat similar to other graph-based meaning representation frameworks.

Related papers

• Scope-enhanced Compositional Semantic Parsing for DRT [52.657454970993086]
  We introduce the AMS Theory, a compositional, neurosymbolic semantic neurosymbolic for Discourse Representation (DRT) We show that the AMS reliably produces well-formed outputs and performs well on DRT parsing, especially on complex sentences.
  arXiv  Detail & Related papers  (2024-07-02T02:50:15Z)
• Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships [69.04014445666142]
  Transitivity Recovering Decompositions (TRD) is a graph-space search algorithm that identifies interpretable equivalents of abstract emergent relationships. We show that TRD is provably robust to noisy views, with empirical evidence also supporting this finding.
  arXiv  Detail & Related papers  (2023-10-24T16:48:56Z)
• Incorporating Graph Information in Transformer-based AMR Parsing [34.461828101932184]
  LeakDistill is a model and method that explores a modification to the Transformer architecture. We show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing.
  arXiv  Detail & Related papers  (2023-06-23T12:12:08Z)
• Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis [201.39323496042527]
  We present a novel bipartite graph reasoning Generative Adversarial Network (BiGraphGAN) for two challenging tasks: person pose and facial image synthesis. The proposed graph generator consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively.
  arXiv  Detail & Related papers  (2022-11-12T18:27:00Z)
• SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation [24.93559076181481]
  Scene graph is structured semantic representation that can be modeled as a form of graph from images and texts. In this paper, we focus on the problem of scene graph parsing from textual description of a visual scene. We design a simple yet effective two-stage scene graph parsing framework utilizing abstract meaning representation.
  arXiv  Detail & Related papers  (2022-10-17T00:37:00Z)
• Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks [10.940340432960294]
  Graph Signal Processing (GSP) has proposed tools to generalize harmonic analysis to complex domains represented through graphs. Among these tools are translations, which are required to define many others. Most works propose to define translations using solely the graph structure (i.e. edges) In this paper, we propose to infer translations as edge-constrained operations that make a supervised classification problem invariant using a deep learning framework.
  arXiv  Detail & Related papers  (2021-02-18T17:21:00Z)
• A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing [75.36126971685034]
  We treat alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. Our method also approaches that of a model that relies on citetLyu2018AMRPA's segmentation rules, which were hand-crafted to handle individual AMR constructions.
  arXiv  Detail & Related papers  (2020-10-23T21:22:50Z)
• The First Shared Task on Discourse Representation Structure Parsing [3.8348270467895915]
  The paper presents the IWCS 2019 shared task on semantic parsing. The goal is to produce Discourse Representation (DRS) for English sentences.
  arXiv  Detail & Related papers  (2020-05-27T14:52:21Z)
• Graph-Structured Referring Expression Reasoning in The Wild [105.95488002374158]
  Grounding referring expressions aims to locate in an image an object referred to by a natural language expression. We propose a scene graph guided modular network (SGMN) to perform reasoning over a semantic graph and a scene graph. We also propose Ref-Reasoning, a large-scale real-world dataset for structured referring expression reasoning.
  arXiv  Detail & Related papers  (2020-04-19T11:00:30Z)
• Bridging Knowledge Graphs to Generate Scene Graphs [49.69377653925448]
  We propose a novel graph-based neural network that iteratively propagates information between the two graphs, as well as within each of them. Our Graph Bridging Network, GB-Net, successively infers edges and nodes, allowing to simultaneously exploit and refine the rich, heterogeneous structure of the interconnected scene and commonsense graphs.
  arXiv  Detail & Related papers  (2020-01-07T23:35:52Z)

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.