Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments

ASQ: Automatically Generating Question-Answer Pairs using AMRs

We introduce ASQ, a tool to automatically mine questions and answers from a sentence using the Abstract Meaning Representation (AMR). Previous work has used question-answer pairs to specify the predicate-argument structure of a sentence using natural language, which does not require linguistic expertise or training, and created datasets such as QA-SRL and QAMR, for which the question-answer pair annotations were crowdsourced. Our goal is to build a tool (ASQ) that maps from the traditional meaning representation AMR to a question-answer meaning representation (QMR). This enables construction o…

ding-01 :ARG0：一个面向法语自发对话的 AMR 语料库

ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue

本文通过扩展 AMR 框架标注法语自发对话语料 DinG，并发布标注指南与语料库，同时训练 AMR 解析器作为辅助标注工具，为法语对话语义资源建设做出贡献。

AMR Parsing as Graph Prediction with Latent Alignment

Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We introduce a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. As exact inference requires marginalizing over alignments and is infeasible, we use the variational auto-encoding framework and a continuous …

GPT-too: A language-model-first approach for AMR-to-text generation

Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10dataset, including the recent use of transformer architectures. In addition to the s…

A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing

Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs to segment the graph into subgraphs and align each such subgraph to a word in a sentence; this is normally done at preprocessing, relying on hand-crafted rules. In contrast, we treat both alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. As marginalizing over the structured latent variables is infeasible, we use the variational autoencoding framework. To ensure…

Semantic Bridge：基于AMR驱动图合成的通用多跳问题生成

Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis

提出Semantic Bridge框架，通过AMR驱动的语义图编织机制从稀疏来源可控生成复杂多跳推理问题，在多语言和领域上显著优于基线与人工标注，为LLM训练数据合成确立新范式。