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# Generation from Abstract Meaning Representation using Tree Transducers

**Authors:** [Jeffrey Flanigan](https://md.mova.elamur.ai/free/en/docs/papers/authors/jeffrey-flanigan/), [Chris Dyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/chris-dyer/), [Noah A. Smith](https://md.mova.elamur.ai/free/en/docs/papers/authors/noah-a-smith/), [Jaime Carbonell](https://md.mova.elamur.ai/free/en/docs/papers/authors/jaime-carbonell/) · **Year:** 2016 · **Venue:** NAACL-HLT 2016 **Link:** [aclanthology.org/N16-1087](https://aclanthology.org/N16-1087/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**97** citations](https://openalex.org/works?filter=cites:W2468355276) openalex · 2026-10-19[**56** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/flanigan-2016-generation-abstract-meaning-representation-tree/) of the 140673 papers whose bibliographies we read[Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies](https://aclanthology.org/volumes/N16-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/N16-1087/)
- [PDF](https://aclanthology.org/N16-1087.pdf)
- [DOI 10.18653/v1/n16-1087](https://doi.org/10.18653/v1/n16-1087)
- [OpenAlex](https://openalex.org/W2468355276)

**Authors (4)**

1. [Jeffrey Flanigan](https://openalex.org/A5068647049) [ORCID](https://orcid.org/0000-0002-8805-9482) · [Semantic Scholar](https://www.semanticscholar.org/author/144683841) · h 13 · 3105 citations · 67 works
2. [Chris Dyer](https://openalex.org/A5111222692) [Semantic Scholar](https://www.semanticscholar.org/author/1745899) · h 76 · 37927 citations · 275 works
3. [Noah A. Smith](https://openalex.org/A5088517824) [ORCID](https://orcid.org/0000-0002-2310-6380) · [Semantic Scholar](https://www.semanticscholar.org/author/144365875) · h 94 · 40232 citations · 486 works
4. [Jaime Carbonell](https://openalex.org/A5109861718) [Semantic Scholar](https://www.semanticscholar.org/author/143712374) · h 64 · 30956 citations · 406 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The paper generates English from AMR graphs in two steps. A breadth-first spanning tree is taken from the graph, and a weighted tree-to-string transducer turns that tree into a sentence. The transducer rules are extracted from AMR-aligned sentences: each aligned subgraph becomes a rule with argument slots for its children, and synthetic rules generalise over concept and argument realisations. Decoding is a linear model over rule features plus a 5-gram language model, tuned with MERT. On AMR 1.0 (about 10,000 training sentences) the full system reaches 22.1 BLEU on test. Removing the synthetic rules drops it to 9.1; removing the basic or abstract rules changes almost nothing.

## Ideas

- I1. Extract a basic rule for each meta token from the span of source words it was read from, with argument slots for its children. This is the same contract as the encoder's context table in `profile.rs`. Test whether the table covers unseen (concept, relation) pairs as well as the paper's synthetic model does.
- I2. Run the translator with each rule source removed in turn (standard table, context table, bare-form fallback) and report the bit cost per class. Each component should earn its place by a measured residual on dev.
- I3. The spanning tree is a fixed breadth-first order with sorted child labels, which the authors call a baseline. Test whether the child order chosen by the tree reader changes the residual; run twice to check identical output.
- I4. The argument rule for a relation such as destination or purpose fixes a preposition. Count (concept, relation) against function words over the Bible pairs in a BTreeMap, and use the result as the function-word feature of open problem 4.

## For Mova

The ideas touch `tools/translate/profile.rs` (encoder), `en/src/lin.rs` (word order) and open problem 4 (function words). The core of the method is a rule table with fallback, which carries over without change. The paper's MERT tuning becomes counts with thresholds, checked on dev.

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/n16-1087](https://doi.org/10.18653/v1/n16-1087)

```
@inproceedings{flanigan2016generation,
  title = {{Generation from Abstract Meaning Representation using Tree Transducers}},
  author = {Flanigan, Jeffrey and Dyer, Chris and Smith, Noah A. and Carbonell, Jaime},
  booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2016},
  doi = {10.18653/v1/n16-1087},
  url = {https://doi.org/10.18653/v1/n16-1087}
}
```

- [← previous StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements](https://md.mova.elamur.ai/free/en/docs/papers/fisher-2024-styleremix-interpretable-authorship-obfuscation-distillation/)
- [next → SEM-I Rational MT: Enriching Deep Grammars with a Semantic Interface for Scalable Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/flickinger-2005-sem-i-rational-mt-enriching/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

## References

25 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013. Abstract meaning representation for sembanking. In Proc. of the 7th Linguistic Annotation Workshop and Interoperability with Discourse. [find](https://scholar.google.com/scholar?q=Laura+Banarescu%2C+Claire+Bonial%2C+Shu+Cai%2C+Madalina+Georgescu%2C+Kira+Griffitt%2C+Ulf+Hermjakob%2C+Kevin+Knight%2C+Philipp+Koehn%2C+Martha+Palmer%2C+and+Nathan+Schneider%2E+2013%2E+Abstract+meaning+representation+for+s) [our card](https://md.mova.elamur.ai/free/en/docs/papers/banarescu-2013-abstract-meaning-representation-sembanking/)
2. Anja Belz, Michael White, Dominic Espinosa, Eric Kow, Deirdre Hogan, and Amanda Stent. 2011. The first surface realisation shared task: Overview and evaluation results. In Proc. of the 13th European Workshop on Natural Language Generation. [find](https://scholar.google.com/scholar?q=Anja+Belz%2C+Michael+White%2C+Dominic+Espinosa%2C+Eric+Kow%2C+Deirdre+Hogan%2C+and+Amanda+Stent%2E+2011%2E+The+first+surface+realisation+shared+task%3A+Overview+and+evaluation+results%2E+In+Proc%2E+of+the+13th+European+W) [our card](https://md.mova.elamur.ai/free/en/docs/papers/belz-2011-first-surface-realisation-shared-task/)
3. Bernd Bohnet, Leo Wanner, Simon Mille, and Alicia Burga. 2010. Broad coverage multilingual deep sentence generation with a stochastic multi-level realizer. In Proc. of COLING. [find](https://scholar.google.com/scholar?q=Bernd+Bohnet%2C+Leo+Wanner%2C+Simon+Mille%2C+and+Alicia+Burga%2E+2010%2E+Broad+coverage+multilingual+deep+sentence+generation+with+a+stochastic+multi%2Dlevel+realizer%2E+In+Proc%2E+of+COLING%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bohnet-2010-broad-coverage-multilingual-deep-sentence/)
4. Aoife Cahill and Josef Van Genabith. 2006. Robust pcfg-based generation using automatically acquired LFG approximations. In Proc. of COLING-ACL. [find](https://scholar.google.com/scholar?q=Aoife+Cahill+and+Josef+Van+Genabith%2E+2006%2E+Robust+pcfg%2Dbased+generation+using+automatically+acquired+LFG+approximations%2E+In+Proc%2E+of+COLING%2DACL%2E)
5. Michael Collins. 2002. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Michael+Collins%2E+2002%2E+Discriminative+training+methods+for+hidden+Markov+models%3A+Theory+and+experiments+with+perceptron+algorithms%2E+In+Proc%2E+of+EMNLP%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/collins-2002-discriminative-training-methods-hidden-markov/)
6. John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. JMLR, 12. [find](https://scholar.google.com/scholar?q=John+Duchi%2C+Elad+Hazan%2C+and+Yoram+Singer%2E+2011%2E+Adaptive+subgradient+methods+for+online+learning+and+stochastic+optimization%2E+JMLR%2C+12%2E)
7. Chris Dyer, Adam Lopez, Juri Ganitkevitch, Johnathan Weese, Ferhan Ture, Phil Blunsom, Hendra Setiawan, Vladimir Eidelman, and Philip Resnik. 2010. cdec: A decoder, alignment, and learning framework for finite-state and context-free translation models. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Chris+Dyer%2C+Adam+Lopez%2C+Juri+Ganitkevitch%2C+Johnathan+Weese%2C+Ferhan+Ture%2C+Phil+Blunsom%2C+Hendra+Setiawan%2C+Vladimir+Eidelman%2C+and+Philip+Resnik%2E+2010%2E+cdec%3A+A+decoder%2C+alignment%2C+and+learning+framework+f)
8. Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A. Smith. 2014. A discriminative graph-based parser for the abstract meaning representation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Jeffrey+Flanigan%2C+Sam+Thomson%2C+Jaime+Carbonell%2C+Chris+Dyer%2C+and+Noah+A%2E+Smith%2E+2014%2E+A+discriminative+graph%2Dbased+parser+for+the+abstract+meaning+representation%2E+In+Proc%2E+of+ACL%2E)
9. Michel Galley, Mark Hopkins, Kevin Knight, and Daniel Marcu. 2004. What’s in a translation rule? In Proc. of HLT-NAACL. [find](https://scholar.google.com/scholar?q=Michel+Galley%2C+Mark+Hopkins%2C+Kevin+Knight%2C+and+Daniel+Marcu%2E+2004%2E+What%E2%80%99s+in+a+translation+rule%3F+In+Proc%2E+of+HLT%2DNAACL%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/galley-2004-what-s-translation-rule/)
10. Joshua Goodman. 1999. Semiring parsing. CL, 25(4). [find](https://scholar.google.com/scholar?q=Joshua+Goodman%2E+1999%2E+Semiring+parsing%2E+CL%2C+25%284%29%2E)
11. Jonathan Graehl and Kevin Knight. 2004. Training tree transducers. In Proc. of HLT-NAACL. [find](https://scholar.google.com/scholar?q=Jonathan+Graehl+and+Kevin+Knight%2E+2004%2E+Training+tree+transducers%2E+In+Proc%2E+of+HLT%2DNAACL%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/graehl-2004-training-tree-transducers/)
12. Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. 2013. Scalable modified Kneser-Ney language model estimation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Kenneth+Heafield%2C+Ivan+Pouzyrevsky%2C+Jonathan+H%2E+Clark%2C+and+Philipp+Koehn%2E+2013%2E+Scalable+modified+Kneser%2DNey+language+model+estimation%2E+In+Proc%2E+of+ACL%2E)
13. Deirdre Hogan, Conor Cafferkey, Aoife Cahill, and Josef Van Genabith. 2007. Exploiting multi-word units in history-based probabilistic generation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Deirdre+Hogan%2C+Conor+Cafferkey%2C+Aoife+Cahill%2C+and+Josef+Van+Genabith%2E+2007%2E+Exploiting+multi%2Dword+units+in+history%2Dbased+probabilistic+generation%2E+In+Proc%2E+of+ACL%2E)
14. Liang Huang, Kevin Knight, and Aravind Joshi. 2006. Statistical syntax-directed translation with extended domain of locality. In Proc. of AMTA. [find](https://scholar.google.com/scholar?q=Liang+Huang%2C+Kevin+Knight%2C+and+Aravind+Joshi%2E+2006%2E+Statistical+syntax%2Ddirected+translation+with+extended+domain+of+locality%2E+In+Proc%2E+of+AMTA%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/huang-2006-statistical-syntax-directed-translation-extended/)
15. Bevan Jones, Jacob Andreas, Daniel Bauer, Karl Moritz Hermann, and Kevin Knight. 2012. Semantics-based machine translation with hyperedge replacement grammars. In Proc. of COLING. [find](https://scholar.google.com/scholar?q=Bevan+Jones%2C+Jacob+Andreas%2C+Daniel+Bauer%2C+Karl+Moritz+Hermann%2C+and+Kevin+Knight%2E+2012%2E+Semantics%2Dbased+machine+translation+with+hyperedge+replacement+grammars%2E+In+Proc%2E+of+COLING%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/jones-2012-semantics-based-machine-translation-hyperedge/)
16. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007. Moses: Open source toolkit for statistical machine translation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2C+Hieu+Hoang%2C+Alexandra+Birch%2C+Chris+Callison%2DBurch%2C+Marcello+Federico%2C+Nicola+Bertoldi%2C+Brooke+Cowan%2C+Wade+Shen%2C+Christine+Moran%2C+Richard+Zens%2C+et+al%2E+2007%2E+Moses%3A+Open+source+toolkit+fo)
17. Irene Langkilde and Kevin Knight. 1998. Generation that exploits corpus-based statistical knowledge. In Proc. of COLING-ACL. [find](https://scholar.google.com/scholar?q=Irene+Langkilde+and+Kevin+Knight%2E+1998%2E+Generation+that+exploits+corpus%2Dbased+statistical+knowledge%2E+In+Proc%2E+of+COLING%2DACL%2E)
18. Irene Langkilde. 2000. Forest-based statistical sentence generation. In Proc. of NAACL 2000. [find](https://scholar.google.com/scholar?q=Irene+Langkilde%2E+2000%2E+Forest%2Dbased+statistical+sentence+generation%2E+In+Proc%2E+of+NAACL+2000%2E)
19. Hiroko Nakanishi, Yusuke Miyao, and Jun’ichi Tsujii. 2005. Probabilistic models for disambiguation of an HPSG-based chart generator. In Proc. of IWPT. [find](https://scholar.google.com/scholar?q=Hiroko+Nakanishi%2C+Yusuke+Miyao%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2005%2E+Probabilistic+models+for+disambiguation+of+an+HPSG%2Dbased+chart+generator%2E+In+Proc%2E+of+IWPT%2E)
20. Franz Josef Och. 2003. Minimum error rate training in statistical machine translation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Franz+Josef+Och%2E+2003%2E+Minimum+error+rate+training+in+statistical+machine+translation%2E+In+Proc%2E+of+ACL%2E)
21. Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The proposition bank: An annotated corpus of semantic roles. CL, 31(1). [find](https://scholar.google.com/scholar?q=Martha+Palmer%2C+Daniel+Gildea%2C+and+Paul+Kingsbury%2E+2005%2E+The+proposition+bank%3A+An+annotated+corpus+of+semantic+roles%2E+CL%2C+31%281%29%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/)
22. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Kishore+Papineni%2C+Salim+Roukos%2C+Todd+Ward%2C+and+Wei%2DJing+Zhu%2E+2002%2E+Bleu%3A+a+method+for+automatic+evaluation+of+machine+translation%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/papineni-2002-bleu-method-automatic-evaluation-machine/)
23. Frank Rosenblatt. 1957. The perceptron—a perceiving and recognizing automaton. Technical Report 85-460-1, Cornell Aeronautical Laboratory. [find](https://scholar.google.com/scholar?q=Frank+Rosenblatt%2E+1957%2E+The+perceptron%E2%80%94a+perceiving+and+recognizing+automaton%2E+Technical+Report+85%2D460%2D1%2C+Cornell+Aeronautical+Laboratory%2E)
24. Michael White, Rajakrishnan Rajkumar, and Scott Martin. 2007. Towards broad coverage surface realization with CCG. In Proc. of the Workshop on Using Corpora for NLG: Language Generation and Machine Translation. [find](https://scholar.google.com/scholar?q=Michael+White%2C+Rajakrishnan+Rajkumar%2C+and+Scott+Martin%2E+2007%2E+Towards+broad+coverage+surface+realization+with+CCG%2E+In+Proc%2E+of+the+Workshop+on+Using+Corpora+for+NLG%3A+Language+Generation+and+Machine+Tr) [our card](https://md.mova.elamur.ai/free/en/docs/papers/white-2007-broad-coverage-surface-realization-ccg/)
25. Yuk Wah Wong and Raymond J Mooney. 2006. Learning for semantic parsing with statistical machine translation. In Proc. of HLT-NAACL. [find](https://scholar.google.com/scholar?q=Yuk+Wah+Wong+and+Raymond+J+Mooney%2E+2006%2E+Learning+for+semantic+parsing+with+statistical+machine+translation%2E+In+Proc%2E+of+HLT%2DNAACL%2E)