---
title: "Multilingual Joint Parsing of Syntactic and Semantic"
description: "The authors therefore propose a joint generative, history-based parser. 74% F1 on CoNLL-2005. 122 references, cited by 32 papers here."
authors: ["James Henderson", "Paola Merlo", "Ivan Titov", "Gabriele Musillo"]
year: 2013
venue: "Computational Linguistics"
source: "https://aclanthology.org/J13-4006/"
doi: "10.1162/coli_a_00158"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/henderson-2013-multilingual-joint-parsing-syntactic-semantic/"
---
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# Multilingual Joint Parsing of Syntactic and Semantic Dependencies with a Latent Variable Model

**Authors:** [James Henderson](https://md.research.elamur.ai/free/en/authors/james-henderson/), [Paola Merlo](https://md.research.elamur.ai/free/en/authors/paola-merlo/), [Ivan Titov](https://md.research.elamur.ai/free/en/authors/ivan-titov/) and Gabriele Musillo · **Year:** 2013 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2013](https://md.research.elamur.ai/free/en/venues/cl/2013/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/J13-4006/) · [PDF](https://aclanthology.org/J13-4006.pdf) · [DOI 10.1162/coli\_a\_00158](https://doi.org/10.1162/coli_a_00158) · [OpenAlex](https://openalex.org/W2137132216)

**Citations:** 65 citations (OpenAlex, 2026-10-14) · [32](https://md.research.elamur.ai/free/en/papers/henderson-2013-multilingual-joint-parsing-syntactic-semantic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses how to recover a sentence's syntactic dependency tree and its semantic role graph together. The semantic graph is a set of predicate-argument treelets that can be disconnected and share arguments. Its arcs do not map one-to-one onto syntactic arcs, and hand-built feature interactions between the two levels are hard to specify and vary by language. The authors therefore propose a joint generative, history-based parser. Syntax uses a shift-reduce derivation, and semantics uses a new derivation scheme suited to its looser graph structure. Both derivations are cut into per-word chunks, each holding every decision about arcs to the left of that word. The syntactic and semantic chunks for the same word are then interleaved, which gives a weak synchronization. Training uses Incremental Sigmoid Belief Networks (ISBN), which learn latent vectors describing the parse history instead of relying on engineered features. This captures correlations within and across both structures and is the first multi-task use of ISBNs. The model is competitive on syntactic and semantic parsing in several languages, and joint learning proves beneficial. The approach applies to any pair of related representations.

## Key points
- **Synchronization rule:** a derivation step belongs to whichever word sits at the front of the queue just before that step. Paired chunks have unbounded and unequal lengths, so they are modeled with parameters rather than atomic synchronous-grammar rules.
- **ISBN architecture:** ISBN (Henderson and Titov 2010) extends Henderson's 2003 latent-variable parser, which had already performed well in constituency and dependency parsing.
- **Multilingual evaluation:** the system was evaluated on several languages in the setting of the CoNLL-2009 shared task.
- **Why joint learning helps:** the authors credit a shared induced latent representation, consistent with theory that pipelines win only when no such representation exists (Roth, Small, and Titov 2009). For context, Collobert et al. (2011) report 74% F1 on CoNLL-2005 without syntax and 76% with it.
- **Follow-up use:** Van der Plas, Merlo, and Henderson (2011) later used the model on semantic annotations obtained by cross-lingual projection, where it improved annotation quality. The authors suggest syntax-based SMT as a further application.

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

## Institutions

- [Saarland University](https://md.research.elamur.ai/free/en/universities/saarland-university/) — University, Saarbrücken, Germany · 148 works · 18,287 citations
- [University of Geneva](https://md.research.elamur.ai/free/en/universities/university-of-geneva/) — University, Geneva, Switzerland · 29 works · 3,158 citations

Also on the byline: [dMetrics](https://ror.org/05s1j9511).

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🧭 Topics (1): [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 828 works

## Cite

Cite as: James Henderson, Paola Merlo, Ivan Titov and Gabriele Musillo. 2013. Multilingual Joint Parsing of Syntactic and Semantic Dependencies with a Latent Variable Model. Computational Linguistics.

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

```bibtex
@article{henderson2013multilingual,
  title = {{Multilingual Joint Parsing of Syntactic and Semantic Dependencies with a Latent Variable Model}},
  author = {James Henderson, Paola Merlo, Ivan Titov and Gabriele Musillo},
  journal = {Computational Linguistics},
  year = {2013},
  doi = {10.1162/coli\_a\_00158},
  url = {https://doi.org/10.1162/coli_a_00158}
}
```

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*Updated 29 October 2026*