Tree-structured decoding with doubly-recurrent neural networks

By David Alvarez-Melis, Tommi S. Jaakkola. 2016

106 citations recorded by the source corpus.

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Citations in the corpus

Tree-to-tree Neural Networks for Program Translation (2018, methodology): “Recently, various neural networks with tree structures have been proposed to employ the structural information of the data [11, 26, 25, 32, 3, 29, 34, 27, 13, 33, 28, 18, 6].”
Coarse-to-Fine Decoding for Neural Semantic Parsing (2018, background): “Examples include tree decoders (Dong and Lapata, 2016; Alvarez-Melis and Jaakkola, 2017), decoders constrained by a grammar model (Xiao et al.”
Syntax-Directed Variational Autoencoder for Structured Data (2018, methodology): “In encoder, we use 3-layer one-dimension convolution neural networks (CNNs) followed by a full connected layer, whose output would be fed into two separate affine layers for producing µ and σ respectively as in reparameterization trick; and in decoder we use 3-layer RNNs followed by a affine layer a”
Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data (2019, methodology): “A large body of work has also focused on how to generate trees [13, 14, 15, 16, 17, 18, 19, 20].”
Grammar-based Neural Text-to-SQL Generation (2019, background): “A relevant line of work in semantic parsing has been treating the problem as a sequence generation task by linearizing trees (Dong and Lapata, 2016; Alvarez-Melis and Jaakkola, 2016).”
Retrieval-Based Neural Code Generation (2018, background): “, 2016) or tree-based architectures (Dong and Lapata, 2016; Alvarez-Melis and Jaakkola, 2017). Ling et al. (2016); Jia and Liang (2016); Locascio et al. (2016) treat semantic parsing as a sequence generation task by linearizing trees. The closest work to ours are Yin and Neubig (2017) and Rabinovich”
Discourse Representation Structure Parsing (2018, background): “…neural network architectures tailored to tree or graph-structured decoding (Dong and La-pata, 2016; Cheng et al., 2017; Yin and Neubig, 2017; Alvarez-Melis and Jaakkola, 2017; Rabi-novich et al., 2017; Buys and Blunsom, 2017) Most previous work focuses on building semantic parsers for…”
Tree-structured Decoding for Solving Math Word Problems (2019, background): “Alvarez-Melis and Jaakkola (2017) presented doubly recurrent neural networks to predict tree topology explicitly.”
Unsupervised Recurrent Neural Network Grammars (2019, background): “…modeling, both for unconditional (Emami and Jelinek, 2005; Buys and Blunsom, 2015; Dyer et al., 2016) and conditional (Yin and Neubig, 2017; Alvarez-Melis and Jaakkola, 2017; Rabi-novich et al., 2017; Aharoni and Goldberg, 2017; Eriguchi et al., 2017; Wang et al., 2018; Gu et al., 2018)…”
Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation (2020, background): “Generative models of graphs and trees [42,17,73,43,10,3] generally produce graphs with richer structure with more flexibility over grammar based models, but often fail to produce syntactically correct graphs for cases with a defined syntax such as programs and scene graphs.”
Novel positional encodings to enable tree-based transformers (2019, methodology): “Later work has proposed generation methods that are more sensitive to tree structures and well-formedness constraints (Dong & Lapata, 2016; Alvarez-Melis & Jaakkola, 2017), leading to new-state-of-the-art results.”
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020, background): “…(e.g. tree structures), many efforts have been devoted to develop structure-oriented decoders, including tree decoders (Dong and Lapata, 2016; Alvarez-Melis and Jaakkola, 2017), grammar constrained decoders (Yin and Neubig, 2017; Yin et al., 2018; Jie and Lu, 2018; Dong and Lapata, 2018),…”