# A tree interpretation of arc standard dependency derivation

arXiv: https://arxiv.org/abs/2603.27459 · HTML: https://mlsanity.com/paper/2603.27459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 29 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [Grammar as a Foreign Language](https://mlsanity.com/paper/1412.7449/) (2014)
- [In-Order Transition-based Constituent Parsing](https://mlsanity.com/paper/1707.05000/) (2017)
- [Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategy](https://mlsanity.com/paper/1804.07961/) (2018)
- [Parsing as Reduction](https://mlsanity.com/paper/1503.00030/) (2015)
- [Neural Transition-based Syntactic Linearization](https://mlsanity.com/paper/1810.09609/) (2018)

## Abstract (arXiv)

Arc-standard derivations over projective dependency trees can be interpreted as the incremental construction of lexicalized ordered trees with contiguous yields. Each textscshift, textscleftarc, and textscrightarc transition corresponds to a deterministic tree update, and the resulting ordered tree uniquely determines the dependency arcs introduced by the derivation. We show that this representation is not an arbitrary encoding: a single-headed dependency tree admits such a contiguous ordered representation if and only if it is projective. The proposal is therefore derivational rather than conversion-based, since the ordered object is defined over the transition sequence itself rather than obtained by transforming a completed dependency graph. This gives a tree-theoretic interpretation of arc-standard parsing, in which projective dependency derivations implicitly construct recoverable constituency-style ordered trees. For non-projective inputs, the interpretation can be used through pseudo-projective lifting and inverse decoding. A small implementation study confirms that the mapped derivations are executable in an existing neural transition-based parser.