---
title: "In-Order Transition-based Constituent Parsing — Liu 2017"
description: "Transition-based constituent parsers usually build trees in one of two orders. 91.8 F1 on WSJ. 38 references, cited by 34 papers here."
authors: ["Jiangming Liu", "Yue Zhang"]
year: 2017
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q17-1029/"
doi: "10.1162/tacl_a_00070"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2017-order-transition-based-constituent-parsing/"
---
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# In-Order Transition-based Constituent Parsing

**Authors:** [Jiangming Liu](https://md.research.elamur.ai/free/en/authors/jiangming-liu/) and [Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/) · **Year:** 2017 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q17-1029/) · [PDF](https://aclanthology.org/Q17-1029.pdf) · [DOI 10.1162/tacl\_a\_00070](https://doi.org/10.1162/tacl_a_00070) · [OpenAlex](https://openalex.org/W2963372751)

**Citations:** 64 citations (OpenAlex, 2026-10-14) · [34](https://md.research.elamur.ai/free/en/papers/liu-2017-order-transition-based-constituent-parsing/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
Transition-based constituent parsers usually build trees in one of two orders. Bottom-up parsers (post-order traversal) can use features from subtrees they have already built, but they have no lookahead about structure still to come. Top-down parsers (pre-order traversal) give global guidance, but they must commit to a phrase label before seeing its contents, so they depend heavily on the input encoder.

Liu and Zhang propose a third option: a transition system based on in-order traversal. The parser first shifts a phrase's leftmost child, then projects the parent label, then attaches the remaining children before closing the constituent. This mirrors how human readers anticipate upcoming phrases, and it avoids the language-specific binarization rules that bottom-up parsing requires. The authors compare all three strategies inside one stack-LSTM framework that follows Dyer et al. (2016).

On the English WSJ benchmark, the in-order parser outperforms both baselines and sets state-of-the-art results when combined with reranking. The work matters as the first controlled neural comparison of traversal orders, and it offers a simple, portable way to combine bottom-up and top-down evidence.

## Key points
- The system uses four actions: SHIFT, PJ-X (project nonterminal X), REDUCE and FINISH. It generalizes to "k-in-order" variants, where top-down corresponds to k=0 and bottom-up to k=∞; only k=1 is studied.
- Greedy fully-supervised parsing scores 91.8 F1 on WSJ (English) and 86.1 F1 on Chinese. Reranking raises the English score to 93.6 F1 (supervised, Choe and Charniak 2016) and 94.2 F1 (semi-supervised).
- After conversion to Stanford dependencies, the output achieves 96.2% UAS and 95.2% LAS, state-of-the-art at the time.
- Per-label analysis shows the in-order parser is best on every constituent type. Among the baselines, top-down does better on WHNP because these phrases begin with a predictable question word.
- Code is released at github.com/LeonCrashCode/InOrderParser.

*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

- [Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) — University, Singapore, Singapore · 20 works · 8,193 citations

💡 Concepts (1): [🌲 Context-free grammar](https://md.research.elamur.ai/free/en/concepts/context-free-grammar/) · 25 works

📚 Shelves (1): [💬 Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) · 2,549 works

🧭 Topics (1): [📐 Syntax & grammar formalisms](https://md.research.elamur.ai/free/en/topics/syntax-grammar/) · 140 works

## Cite

Cite as: Jiangming Liu and Yue Zhang. 2017. In-Order Transition-based Constituent Parsing. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{liu2017order,
  title = {{In-Order Transition-based Constituent Parsing}},
  author = {Jiangming Liu and Yue Zhang},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2017},
  doi = {10.1162/tacl\_a\_00070},
  url = {https://doi.org/10.1162/tacl_a_00070}
}
```

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## References

38 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 23 October 2026*