Papers AMR-to-Text Generation
“AMR-to-Text Generation” 태그가 달린 논문 36편 · 필터 해제
Generating Text from Uniform Meaning Representation
Uniform Meaning Representation (UMR) is a recently developed graph-based semantic representation, which expands on Abstract Meaning Representation (AMR) in a number of ways, in particular through the inclusion of documen…
Abstract Meaning RepresentationAMR-to-Text GenerationText GenerationInvestigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters
Text generation from Abstract Meaning Representation (AMR) has substantially benefited from the popularized Pretrained Language Models (PLMs). Myriad approaches have linearized the input graph as a sequence of tokens to …
Abstract Meaning RepresentationAMR-to-Text GenerationLink PredictionPosition+1BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning
Abstract Meaning Representation (AMR) offers a unified semantic representation for natural language sentences. Thus transformation between AMR and text yields two transition tasks in opposite directions, i.e., Text-to-AM…
Abstract Meaning RepresentationAMR ParsingAMR-to-Text GenerationText GenerationInterpretable AMR-Based Question Decomposition for Multi-hop Question Answering
Effective multi-hop question answering (QA) requires reasoning over multiple scattered paragraphs and providing explanations for answers. Most existing approaches cannot provide an interpretable reasoning process to illu…
Abstract Meaning RepresentationAMR-to-Text GenerationMulti-hop Question AnsweringQuestion Answering+1Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation
A common way to combat exposure bias is by applying scores from evaluation metrics as rewards in reinforcement learning (RL). Metrics leveraging contextualized embeddings appear more flexible than their n-gram matching c…
AMR-to-Text GenerationReinforcement Learning (RL)Semantic SimilaritySemantic Textual Similarity+1Graph Pre-training for AMR Parsing and Generation
Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, res…
Abstract Meaning RepresentationAMR ParsingAMR-to-Text GenerationText GenerationGraph Pre-training for AMR Parsing and Generation
Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation. How…
Abstract Meaning RepresentationAMR ParsingAMR-to-Text GenerationText GenerationAMR-to-text Generation with Graph Structure Reconstruction and Coverage
Generating text from semantic representations such as AMR is a challenging task. Previous research formalizes this task as a graph-to-sequence learning problem and uses various graph neural networks to model the graph st…
AMR-to-Text GenerationGraph-to-SequenceText GenerationTranslationSmelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generated AMRs, potentially limiting the transfe…
AMR-to-Text GenerationData AugmentationText GenerationLatent Tree Decomposition Parsers for AMR-to-Text Generation
Graph encoders in AMR-to-text generation models often rely on neighborhood convolutions or global vertex attention. While these approaches apply to general graphs, AMRs may be amenable to encoders that target their tree-…
AMR-to-Text GenerationClusteringMolecular Property PredictionProperty Prediction+2Tree Decomposition Attention for AMR-to-Text Generation
Text generation from AMR requires mapping a semantic graph to a string that it annotates. Transformer-based graph encoders, however, poorly capture vertex dependencies that may benefit sequence prediction. To impose orde…
AMR-to-Text GenerationText GenerationAvoiding Overlap in Data Augmentation for AMR-to-Text Generation
Leveraging additional unlabeled data to boost model performance is common practice in machine learning and natural language processing. For generation tasks, if there is overlap between the additional data and the target…
AMR-to-Text GenerationData AugmentationText GenerationXLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation
Due to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and English-to- X…
Abstract Meaning RepresentationAMR ParsingAMR-to-Text GenerationMachine Translation+2One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline
In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.e., a set of content-specific heuris…
AMR ParsingAMR-to-Text GenerationDecoderSemantic Parsing+1Structural Adapters in Pretrained Language Models for AMR-to-text Generation
Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the g…
AMR-to-Text GenerationData-to-Text GenerationText GenerationGeneralized Shortest-Paths Encoders for AMR-to-Text Generation
For text generation from semantic graphs, past neural models encoded input structure via gated convolutions along graph edges. Although these operations provide local context, the distance messages can travel is bounded …
AMR-to-Text GenerationDiversityText GenerationMultilingual AMR-to-Text Generation
Generating text from structured data is challenging because it requires bridging the gap between (i) structure and natural language (NL) and (ii) semantically underspecified input and fully specified NL output. Multiling…
AMR-to-Text GenerationText GenerationOnline Back-Parsing for AMR-to-Text Generation
AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on stand…
AMR-to-Text GenerationData-to-Text GenerationDecoderLanguage Modeling+3Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph representations. Previously, Graph Convolution …
Abstract Meaning RepresentationAMR-to-Text GenerationText Generation融合目标端句法的AMR-to-Text生成(AMR-to-Text Generation with Target Syntax)
抽象语义表示到文本(AMR-to-Text)生成的任务是给定AMR图,生成相同语义表示的文本。可以把此任务当作一个从源端AMR图到目标端句子的机器翻译任务。目前存在的一些方法都在探索如何更好的对图结构进行建模。然而,它们都存在一个未限定的问题,因为在生成阶段许多句法的决策并不受语义图的约束,从而忽略了句子内部潜藏的句法信息。为了明确考虑这一不足,该文提出一种直接而有效的方法,显示的在AMR-to-Text生成的任务中融入句法信息,并在T…
AMR-to-Text GenerationText Generation