[PDF] Top 20 A Graph to Sequence Model for AMR to Text Generation
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A Graph to Sequence Model for AMR to Text Generation
... Open-class tokens, such as dates, numbers and named entities, account for a large portion in the AMR corpus. Most appear only a few times, re- sulting in a data sparsity problem. To address this issue, Konstas et ... See full document
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Modeling Graph Structure in Transformer for Better AMR to Text Generation
... on AMR-to-text generation often formalize the task as a sequence-to- sequence (seq2seq) learning problem by con- verting an Abstract Meaning Representation (AMR) graph ... See full document
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Enhancing AMR to Text Generation with Dual Graph Representations
... for AMR-to-text ...the model capacity and employ anonymization. How- ever, our model is substantially different: (i) we learn dual representations capturing top-down and bottom-up adjuvant ... See full document
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Structural Neural Encoders for AMR to text Generation
... and graph encoding (Song et ...median model, according to their BLEU score on the development set (Beck et ...and graph encoders, demonstrating the efficacy of our GCNSeq ...The graph encoder ... See full document
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AMR to text generation as a Traveling Salesman Problem
... of AMR-to-text generation is to gen- erate grammatical text that sustains the seman- tic meaning for a given AMR ...the AMR graph into smaller fragments, and then gener- ... See full document
6
AMR to text Generation with Synchronous Node Replacement Grammar
... given AMR graph into a spanning tree, before translating it to a sentence using a tree-to-string ...the graph-to-tree trans- formation step cannot be ...using graph- fragment-to-string rules. ... See full document
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Neural AMR: Sequence to Sequence Models for Parsing and Generation
... of text and replace mapped text with the anonymized token that we inserted into the AMR ...of generation models. If a gener- ation model predicts an anonymization token, we find the ... See full document
12
Coherent Comments Generation for Chinese Articles with a Graph to Sequence Model
... the generation tasks. Xu et al. (2018a,b) proposed to use graph based model to encode SQL queries in the SQL-to-Text ...the AMR-to-Text problem with graph neural ... See full document
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Improving Language Generation from Feature Rich Tree Structured Data with Relational Graph Convolutional Encoders
... acyclic graph, surface re- alization is a graph-to-text generation ...tasks. Graph neural networks have been successfully applied to different graph to text ... See full document
6
Denoising based Sequence to Sequence Pre training for Text Generation
... new sequence-to- sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation ...noise-corrupted text, ... See full document
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Linguistic realisation as machine translation: Comparing different MT models for AMR to text generation
... Inspection of the LDC2016E25 corpus reveals that on average 22% of the structure of an AMR are AMR constant values, such as names, quantities, and dates. This information increases the sparsity of the data, ... See full document
10
Deep Learning Approach for Text Generation Using RNN Encoder-Decoder for Q&A
... output sequence. The model proposed leads to improved BLEU score for statistical machine ...Proposed model consists three RNNs, encoder encodes the input sentences and decoder that generates ... See full document
6
Text Generation from Knowledge Graphs with Graph Transformers
... knowledge graph which collapses co-referential entities. An example of a text and graph are shown in Figure ...these graph/text pairs to train a novel attention-based encoder-decoder ... See full document
10
Procedural Text Generation from a Photo Sequence
... Thus we assume a photo sequence as the input. Since authors of multimedia procedural texts at least take a photo at each important step, this set- ting is realistic. Sharing the input and output me- dia the most ... See full document
6
Automatic Grammatical Error Correction for Sequence to sequence Text Generation: An Empirical Study
... the text style transfer ...transformer model as our base model and train a model with approximately 100K paral- lel sentences in the GYAFC corpus for informal→ informal and formal → informal ... See full document
6
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
... open-source sequence model- ing toolkit that allows researchers and devel- opers to train custom models for translation, summarization, language modeling, and other text generation ... See full document
6
Graph Enhanced Cross Domain Text to SQL Generation
... on text- to-SQL parsing. However, most text-to-SQL parsers do not generalize to unseen data sets in different ...form text-to-SQL translation and demonstrate its use on Spider, a large-scale ... See full document
5
Generation of Conceptual-Level Text Cloud with Graph Diffusion
... well-known text cloud visualiza- tion with the conceptual ...traditional text cloud is usually generated according to the word occurrence, possibly including the idf-based concept for word ...a ... See full document
10
AMR Parsing as Sequence to Graph Transduction
... language text into AMR, a graph- based formalism used for capturing sentence-level ...in AMR parsing include: (1) its property of reentrancy – the same concept can participate in multiple ... See full document
15
Factorising AMR generation through syntax
... information AMR contains about possible syntactic ...the AMR graph or the ...neither model is guaranteed to produce trees with the right number of terminals, we first run an insert/delete ... See full document
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