A tree interpretation of arc standard dependency derivation
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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.
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References · 7
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
- 2016Recurrent Neural Network Grammarscited by 109
- 2014Grammar as a Foreign Languagecited by 70
- 2017In-Order Transition-based Constituent Parsingcited by 23
- 2018Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategycited by 8
- 2015Parsing as Reductioncited by 6
- 2018Neural Transition-based Syntactic Linearizationcited by 3
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