---
title: "Encoding Sentences with Graph — Marcheggiani 2017"
description: "The paper tackles dependency-based semantic role labeling (SRL), which involves finding predicates, disambiguating their senses and assigning semantic…"
authors: ["Diego Marcheggiani", "Ivan Titov"]
year: 2017
venue: "arXiv:1703.04826"
source: "https://arxiv.org/abs/1703.04826"
doi: "10.48550/arxiv.1703.04826"
arxiv: "1703.04826"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2017](https://md.research.elamur.ai/free/en/papers/2017/page/2/) / Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

# Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

**Authors:** [Diego Marcheggiani](https://md.research.elamur.ai/free/en/authors/diego-marcheggiani/), [Ivan Titov](https://md.research.elamur.ai/free/en/authors/ivan-titov/) · **Year:** 2017 · **Venue:** arXiv:1703.04826 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1703.04826) · [PDF](https://arxiv.org/pdf/1703.04826) · [DOI 10.48550/arxiv.1703.04826](https://doi.org/10.48550/arxiv.1703.04826) · [arXiv 1703.04826](https://arxiv.org/abs/1703.04826) · [OpenAlex](https://openalex.org/W2600702321)

**Citations:** 874 citations (openalex-snapshot · 2026-10-14) · [227](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tackles dependency-based semantic role labeling (SRL), which involves finding predicates, disambiguating their senses and assigning semantic roles to their arguments. Earlier neural SRL systems built on LSTMs had largely dropped syntax, partly because there was no easy way to inject it at the word level. The authors adapt graph convolutional networks (GCNs) to labeled, directed dependency trees. Information flows both from heads to dependents and back, and a self-loop is included. Weight matrices depend only on edge direction, while syntactic function labels enter through bias terms. A scalar gate on each edge lets the model reduce the influence of parser edges that are unhelpful or wrong. The GCN layers are stacked on top of a BiLSTM encoder, so the LSTM covers long-distance context and the GCN adds a few hops of syntactic neighbourhood. Evaluated on CoNLL-2009, this combination beats prior systems for both English and Chinese. The work was among the first to show that GCNs are useful in NLP. It also demonstrates that sequential and syntax-based encoders complement each other.

## Key points
- Benchmark: English and Chinese portions of CoNLL-2009. The code is available at github.com/diegma/neural-dep-srl.
- The model is kept compact for parameter efficiency:
  - There are only three matrix types per layer (along-arc, opposite-arc and self-loop).
  - Fully label-specific weights would have required 97 matrices per layer for English (48 syntactic functions) and 83 for Chinese (41).
- English in-domain: an ensemble of 3 models (product of experts) reaches 89.1% F1, about 1.2 points above the best previous result.
- Chinese: the best model beats the earlier state of the art, Roth and Lapata (2016), by 3.1 points.
- English out-of-domain (parser output is noisier there):
  - The ensemble scores 78.9 F1 and surpasses all earlier syntax-aware systems.
  - The single local model (77.2) roughly matches, but does not beat, the 77.3 achieved by Marcheggiani et al. (2017), whose model uses no syntax.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/)

## Cited by

Cited by

227 papers of our library cite it (of the 140673 whose bibliographies we read); 18 of them have a page here, 209 not yet.

- [Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/bastings-2017-graph-convolutional-encoders-syntax-aware/)
- [Graph-to-Sequence Learning using Gated Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/beck-2018-graph-sequence-learning-gated-graph/)
- [Attention Guided Graph Convolutional Networks for Relation Extraction](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/)
- [Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network](https://md.research.elamur.ai/free/en/papers/jiang-2021-incorporating-syntax-semantics-coreference-resolution/)
- [Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations](https://md.research.elamur.ai/free/en/papers/lee-2020-weakly-supervised-modeling-contextualized-event/)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://md.research.elamur.ai/free/en/papers/liu-2018-jointly-multiple-events-extraction-attention/)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://md.research.elamur.ai/free/en/papers/nan-2020-reasoning-latent-structure-refinement-document/)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/shi-2019-simple-bert-models-relation-extraction/)
- [Semantic Role Labeling as Syntactic Dependency Parsing](https://md.research.elamur.ai/free/en/papers/shi-2020-semantic-role-labeling-as-syntactic/)
- [A Graph-to-Sequence Model for AMR-to-Text Generation](https://md.research.elamur.ai/free/en/papers/song-2018-graph-sequence-model-amr-text/)
- [Linguistically-Informed Self-Attention for Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/strubell-2018-linguistically-informed-self-attention-semantic/)
- [Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree](https://md.research.elamur.ai/free/en/papers/sun-2019-aspect-level-sentiment-analysis-convolution/)
- [Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement](https://md.research.elamur.ai/free/en/papers/ud-2021-mohammadshahi-recursive-non-autoregressive-graph-graph/)
- [A Comprehensive Survey on Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
- [A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech](https://md.research.elamur.ai/free/en/papers/yang-2021-joint-model-dropped-pronoun-recovery/)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhang-2018-graph-convolution-over-pruned-dependency/)
- [Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://md.research.elamur.ai/free/en/papers/zhang-2019-aspect-based-sentiment-classification-aspect/)
- [A Double-Graph Based Framework for Frame Semantic Parsing](https://md.research.elamur.ai/free/en/papers/zheng-2022-double-graph-based-framework-frame/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{marcheggiani2017encoding,
  title = {{Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling}},
  author = {Marcheggiani, Diego and Titov, Ivan},
  year = {2017},
  doi = {10.48550/arxiv.1703.04826},
  eprint = {1703.04826},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1703.04826}
}
```

- [← previous Empower Sequence Labeling with Task-Aware Neural Language Model](https://md.research.elamur.ai/free/en/papers/liu-2017-empower-sequence-labeling-task-aware/)
- [next → End-to-end Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/lee-2017-end-end-neural-coreference-resolution/)
- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/2/)

## References

46 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research 3:1137–1155. [find](https://scholar.google.com/scholar?q=Yoshua+Bengio%2C+Re%CC%81jean+Ducharme%2C+Pascal+Vincent%2C+and+Christian+Janvin%2E+2003%2E+A+neural+probabilistic+language+model%2E+Journal+of+Machine+Learning+Research+3%3A1137%E2%80%931155%2E)
2. Anders Björkelund, Bernd Bohnet, Love Hafdell, and Pierre Nugues. 2010. A high-performance syntactic and semantic dependency parser. In Proceedings of COLING: Demonstrations. [find](https://scholar.google.com/scholar?q=Anders+Bjo%CC%88rkelund%2C+Bernd+Bohnet%2C+Love+Hafdell%2C+and+Pierre+Nugues%2E+2010%2E+A+high%2Dperformance+syntactic+and+semantic+dependency+parser%2E+In+Proceedings+of+COLING%3A+Demonstrations%2E)
3. Anders Björkelund, Love Hafdell, and Pierre Nugues. 2009. Multilingual semantic role labeling. In Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=Anders+Bjo%CC%88rkelund%2C+Love+Hafdell%2C+and+Pierre+Nugues%2E+2009%2E+Multilingual+semantic+role+labeling%2E+In+Proceedings+of+CoNLL%2E) [our card](https://md.research.elamur.ai/free/en/papers/bjorkelund-2009-multilingual-semantic-role-labeling/)
4. Jason P. C. Chiu and Eric Nichols. 2016. Named entity recognition with bidirectional LSTM-CNNs. TACL 4:357–370. [↗](https://doi.org/10.1162/tacl_a_00104) [our card](https://md.research.elamur.ai/free/en/papers/chiu-2015-named-entity-recognition-bidirectional-lstm/)
5. Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of EMNLP. [↗](https://doi.org/10.48550/arxiv.1406.1078)
6. Yann N. Dauphin, Angela Fan, Michael Auli, and David Grangier. 2016. Language modeling with gated convolutional networks. arXiv preprint arXiv:1612.08083 . David K. Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In Proceedings of NIPS. [↗](https://arxiv.org/abs/1612.08083) [our card](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
7. Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith. 2015. Transition-based dependency parsing with stack long short-term memory. In Proceedings of ACL. [find](https://scholar.google.com/scholar?q=Chris+Dyer%2C+Miguel+Ballesteros%2C+Wang+Ling%2C+Austin+Matthews%2C+and+Noah+A%2E+Smith%2E+2015%2E+Transition%2Dbased+dependency+parsing+with+stack+long+short%2Dterm+memory%2E+In+Proceedings+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/missing-2015-dyer-transition-based-dependency-parsing-stack/)
8. Jeffrey L. Elman. 1990. Finding structure in time. Cognitive Science 14(2):179–211. [↗](https://doi.org/10.1207/s15516709cog1402_1)
9. Akiko Eriguchi, Yoshimasa Tsuruoka, and Kyunghyun Cho. 2017. Learning to parse and translate improves neural machine translation. arXiv preprint arXiv:1702.03525 . [↗](https://arxiv.org/abs/1702.03525) [our card](https://md.research.elamur.ai/free/en/papers/eriguchi-2017-learning-parse-translate-improves-neural/)
10. Nicholas FitzGerald, Oscar Täckström, Kuzman Ganchev, and Dipanjan Das. 2015. Semantic role labeling with neural network factors. In Proceedings of EMNLP. [find](https://scholar.google.com/scholar?q=Nicholas+FitzGerald%2C+Oscar+Ta%CC%88ckstro%CC%88m%2C+Kuzman+Ganchev%2C+and+Dipanjan+Das%2E+2015%2E+Semantic+role+labeling+with+neural+network+factors%2E+In+Proceedings+of+EMNLP%2E)
11. William Foland and James Martin. 2015. Dependency-based semantic role labeling using convolutional neural networks. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics (*SEM). [find](https://scholar.google.com/scholar?q=William+Foland+and+James+Martin%2E+2015%2E+Dependency%2Dbased+semantic+role+labeling+using+convolutional+neural+networks%2E+In+Proceedings+of+the+Fourth+Joint+Conference+on+Lexical+and+Computational+Semantics)
12. Andrea Gesmundo, James Henderson, Paola Merlo, and Ivan Titov. 2009. Latent variable model of synchronous syntactic-semantic parsing for multiple languages. In Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=Andrea+Gesmundo%2C+James+Henderson%2C+Paola+Merlo%2C+and+Ivan+Titov%2E+2009%2E+Latent+variable+model+of+synchronous+syntactic%2Dsemantic+parsing+for+multiple+languages%2E+In+Proceedings+of+CoNLL%2E)
13. Daniel Gildea and Daniel Jurafsky. 2002. Automatic labeling of semantic roles. Computational linguistics 28(3):245–288. [↗](https://doi.org/10.1162/089120102760275983) [our card](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/)
14. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212 . [↗](https://arxiv.org/abs/1704.01212) [our card](https://md.research.elamur.ai/free/en/papers/gilmer-2017-neural-message-passing-quantum-chemistry/)
15. Alex Graves. 2008. Supervised sequence labelling with recurrent neural networks. Ph.D. thesis, München, Techn. [find](https://scholar.google.com/scholar?q=Alex+Graves%2E+2008%2E+Supervised+sequence+labelling+with+recurrent+neural+networks%2E+Ph%2ED%2E+thesis%2C+Mu%CC%88nchen%2C+Techn%2E)
16. James Henderson, Paola Merlo, Gabriele Musillo, and Ivan Titov. 2008. A latent variable model of synchronous parsing for syntactic and semantic dependencies. In Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=James+Henderson%2C+Paola+Merlo%2C+Gabriele+Musillo%2C+and+Ivan+Titov%2E+2008%2E+A+latent+variable+model+of+synchronous+parsing+for+syntactic+and+semantic+dependencies%2E+In+Proceedings+of+CoNLL%2E)
17. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9(8):1735–1780. [↗](https://doi.org/10.1162/neco.1997.9.8.1735)
18. Richard Johansson and Pierre Nugues. 2008. The effect of syntactic representation on semantic role labeling. In Proceedings of COLING. [find](https://scholar.google.com/scholar?q=Richard+Johansson+and+Pierre+Nugues%2E+2008%2E+The+effect+of+syntactic+representation+on+semantic+role+labeling%2E+In+Proceedings+of+COLING%2E)
19. Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley. 2016. Molecular graph convolutions: moving beyond fingerprints. Journal of computer-aided molecular design 30(8):595–608. [↗](https://doi.org/10.1007/s10822-016-9938-8)
20. Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of ICLR. [↗](https://doi.org/10.48550/arxiv.1412.6980)
21. Eliyahu Kiperwasser and Yoav Goldberg. 2016. Simple and accurate dependency parsing using bidirectional LSTM feature representations. TACL 4:313– 327. [↗](https://doi.org/10.1162/tacl_a_00101) [our card](https://md.research.elamur.ai/free/en/papers/narrative-2016-kiperwasser-simple-accurate-dependency-parsing-bidirectional/)
22. Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR. [↗](https://doi.org/10.48550/arxiv.1609.02907) [our card](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/)
23. Phong Le and Willem Zuidema. 2014. The inside-outside recursive neural network model for dependency parsing. In Proceedings of EMNLP. [find](https://scholar.google.com/scholar?q=Phong+Le+and+Willem+Zuidema%2E+2014%2E+The+inside%2Doutside+recursive+neural+network+model+for+dependency+parsing%2E+In+Proceedings+of+EMNLP%2E)
24. Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. 2001. Gradient-based learning applied to document recognition. In Proceedings of Intelligent Signal Processing. [find](https://scholar.google.com/scholar?q=Yann+LeCun%2C+Leon+Bottou%2C+Yoshua+Bengio%2C+and+Patrick+Haffner%2E+2001%2E+Gradient%2Dbased+learning+applied+to+document+recognition%2E+In+Proceedings+of+Intelligent+Signal+Processing%2E)
25. Tao Lei, Yuan Zhang, Lluı́s Màrquez, Alessandro Moschitti, and Regina Barzilay. 2015. High-order low-rank tensors for semantic role labeling. In Proceedings of NAACL. [find](https://scholar.google.com/scholar?q=Tao+Lei%2C+Yuan+Zhang%2C+Llu%C4%B1%CC%81s+Ma%CC%80rquez%2C+Alessandro+Moschitti%2C+and+Regina+Barzilay%2E+2015%2E+High%2Dorder+low%2Drank+tensors+for+semantic+role+labeling%2E+In+Proceedings+of+NAACL%2E)
26. Beth Levin. 1993. English verb classes and alternations: A preliminary investigation. University of Chicago press. [find](https://scholar.google.com/scholar?q=Beth+Levin%2E+1993%2E+English+verb+classes+and+alternations%3A+A+preliminary+investigation%2E+University+of+Chicago+press%2E)
27. Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016. Gated graph sequence neural networks. In Proceedings of ICLR. [↗](https://doi.org/10.48550/arxiv.1511.05493)
28. Wang Ling, Chris Dyer, Alan W Black, and Isabel Trancoso. 2015. Two/too simple adaptations of word2vec for syntax problems. In Proceedings of NAACL. [find](https://scholar.google.com/scholar?q=Wang+Ling%2C+Chris+Dyer%2C+Alan+W+Black%2C+and+Isabel+Trancoso%2E+2015%2E+Two%2Ftoo+simple+adaptations+of+word2vec+for+syntax+problems%2E+In+Proceedings+of+NAACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/ling-2015-two-too-simple-adaptations-word2vec/)
29. Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016. Assessing the ability of LSTMs to learn syntax-sensitive dependencies. TACL 4:521–535. [↗](https://doi.org/10.1162/tacl_a_00115) [our card](https://md.research.elamur.ai/free/en/papers/linzen-2016-assessing-ability-lstms-learn-syntax/)
30. Diego Marcheggiani, Anton Frolov, and Ivan Titov. 2017. A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling. arXiv preprint arXiv:1701.02593 . [↗](https://arxiv.org/abs/1701.02593) [our card](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-simple-accurate-syntax-agnostic-neural/)
31. Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Proceedings of INTERSPEECH. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Martin+Karafia%CC%81t%2C+Luka%CC%81s+Burget%2C+Jan+Cernocky%CC%81%2C+and+Sanjeev+Khudanpur%2E+2010%2E+Recurrent+neural+network+based+language+model%2E+In+Proceedings+of+INTERSPEECH%2E)
32. Makoto Miwa and Mohit Bansal. 2016. End-to-end relation extraction using lstms on sequences and tree structures. In Proceedings of ACL. [find](https://scholar.google.com/scholar?q=Makoto+Miwa+and+Mohit+Bansal%2E+2016%2E+End%2Dto%2Dend+relation+extraction+using+lstms+on+sequences+and+tree+structures%2E+In+Proceedings+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/miwa-2016-end-end-relation-extraction-lstms/)
33. Alessandro Moschitti, Daniele Pighin, and Roberto Basili. 2008. Tree kernels for semantic role labeling. Computational Linguistics 34(2):193–224. [↗](https://doi.org/10.1162/coli.2008.34.2.193)
34. Lili Mou, Hao Peng, Ge Li, Yan Xu, Lu Zhang, and Zhi Jin. 2015. Discriminative neural sentence modeling by tree-based convolution. In Proceedings of EMNLP. [↗](https://doi.org/10.60692/8s20s-bhj49)
35. Sameer Pradhan, Kadri Hacioglu, Wayne H. Ward, James H. Martin, and Daniel Jurafsky. 2005. Semantic role chunking combining complementary syntactic views. In Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=Sameer+Pradhan%2C+Kadri+Hacioglu%2C+Wayne+H%2E+Ward%2C+James+H%2E+Martin%2C+and+Daniel+Jurafsky%2E+2005%2E+Semantic+role+chunking+combining+complementary+syntactic+views%2E+In+Proceedings+of+CoNLL%2E)
36. Vasin Punyakanok, Dan Roth, and Wen-tau Yih. 2008. The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics 34(2):257–287. [↗](https://doi.org/10.1162/coli.2008.34.2.257) [our card](https://md.research.elamur.ai/free/en/papers/punyakanok-2008-importance-syntactic-parsing-inference-semantic/)
37. Michael Roth and Mirella Lapata. 2016. Neural semantic role labeling with dependency path embeddings. In Proceedings of ACL. [↗](https://doi.org/10.48550/arxiv.1605.07515) [our card](https://md.research.elamur.ai/free/en/papers/roth-2016-neural-semantic-role-labeling-dependency/)
38. Rico Sennrich and Barry Haddow. 2016. Linguistic input features improve neural machine translation. In Proceedings of WMT. [find](https://scholar.google.com/scholar?q=Rico+Sennrich+and+Barry+Haddow%2E+2016%2E+Linguistic+input+features+improve+neural+machine+translation%2E+In+Proceedings+of+WMT%2E) [our card](https://md.research.elamur.ai/free/en/papers/sennrich-2016-linguistic-input-features-improve-neural/)
39. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of EMNLP. [find](https://scholar.google.com/scholar?q=Richard+Socher%2C+Alex+Perelygin%2C+Jean+Wu%2C+Jason+Chuang%2C+Christopher+D%2E+Manning%2C+Andrew+Ng%2C+and+Christopher+Potts%2E+2013%2E+Recursive+deep+models+for+semantic+compositionality+over+a+sentiment+treebank%2E+In)
40. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of NIPS. [↗](https://doi.org/10.48550/arxiv.1409.3215)
41. Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2016. Greedy, joint syntactic-semantic parsing with stack LSTMs. In Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=Swabha+Swayamdipta%2C+Miguel+Ballesteros%2C+Chris+Dyer%2C+and+Noah+A%2E+Smith%2E+2016%2E+Greedy%2C+joint+syntactic%2Dsemantic+parsing+with+stack+LSTMs%2E+In+Proceedings+of+CoNLL%2E)
42. Cynthia A. Thompson, Roger Levy, and Christopher D. Manning. 2003. A generative model for semantic role labeling. In Proceedings of ECML. [find](https://scholar.google.com/scholar?q=Cynthia+A%2E+Thompson%2C+Roger+Levy%2C+and+Christopher+D%2E+Manning%2E+2003%2E+A+generative+model+for+semantic+role+labeling%2E+In+Proceedings+of+ECML%2E)
43. Ivan Titov, James Henderson, Paola Merlo, and Gabriele Musillo. 2009. Online projectivisation for synchronous parsing of semantic and syntactic dependencies. In Proceedings of IJCAI. [find](https://scholar.google.com/scholar?q=Ivan+Titov%2C+James+Henderson%2C+Paola+Merlo%2C+and+Gabriele+Musillo%2E+2009%2E+Online+projectivisation+for+synchronous+parsing+of+semantic+and+syntactic+dependencies%2E+In+Proceedings+of+IJCAI%2E)
44. Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, and Alex Graves. 2016. Conditional image generation with PixelCNN decoders. In Proceedings of NIPS. [↗](https://doi.org/10.48550/arxiv.1606.05328)
45. Hai Zhao, Wenliang Chen, Jun’ichi Kazama, Kiyotaka Uchimoto, and Kentaro Torisawa. 2009. Multilingual dependency learning: Exploiting rich features for tagging syntactic and semantic dependencies. In Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=Hai+Zhao%2C+Wenliang+Chen%2C+Jun%E2%80%99ichi+Kazama%2C+Kiyotaka+Uchimoto%2C+and+Kentaro+Torisawa%2E+2009%2E+Multilingual+dependency+learning%3A+Exploiting+rich+features+for+tagging+syntactic+and+semantic+dependencies%2E+In+)
46. Jie Zhou and Wei Xu. 2015. End-to-end learning of semantic role labeling using recurrent neural networks. In Proceedings of ACL. [find](https://scholar.google.com/scholar?q=Jie+Zhou+and+Wei+Xu%2E+2015%2E+End%2Dto%2Dend+learning+of+semantic+role+labeling+using+recurrent+neural+networks%2E+In+Proceedings+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/zhou-2015-end-end-learning-semantic-role/)