Search Results for author: Milo{\v{s}} Stanojevi{\'c}

Found 20 papers, 5 papers with code

Span-Based LCFRS-2 Parsing

no code implementations WS 2020 Milo{\v{s}} Stanojevi{\'c}, Mark Steedman

Concretely, by using a grammar formalism to restrict the space of possible trees we can use dynamic programming parsing algorithms for exact search for the most probable tree.

Sentence

Max-Margin Incremental CCG Parsing

no code implementations ACL 2020 Milo{\v{s}} Stanojevi{\'c}, Mark Steedman

Incremental syntactic parsing has been an active research area both for cognitive scientists trying to model human sentence processing and for NLP researchers attempting to combine incremental parsing with language modelling for ASR and MT.

Language Modelling Sentence

CCG Parsing Algorithm with Incremental Tree Rotation

1 code implementation NAACL 2019 Milo{\v{s}} Stanojevi{\'c}, Mark Steedman

The main obstacle to incremental sentence processing arises from right-branching constituent structures, which are present in the majority of English sentences, as well as optional constituents that adjoin on the right, such as right adjuncts and right conjuncts.

Sentence

The Active-Filler Strategy in a Move-Eager Left-Corner Minimalist Grammar Parser

no code implementations WS 2019 Tim Hunter, Milo{\v{s}} Stanojevi{\'c}, Edward Stabler

Recent psycholinguistic evidence suggests that human parsing of moved elements is {`}active{'}, and perhaps even {`}hyper-active{'}: it seems that a leftward-moved object is related to a verbal position rapidly, perhaps even before the transitivity information associated with the verb is available to the listener.

Human Parsing Position

Neural Discontinuous Constituency Parsing

no code implementations EMNLP 2017 Milo{\v{s}} Stanojevi{\'c}, Raquel G. Alhama

One of the most pressing issues in discontinuous constituency transition-based parsing is that the relevant information for parsing decisions could be located in any part of the stack or the buffer.

Constituency Parsing

Universal Reordering via Linguistic Typology

no code implementations COLING 2016 Joachim Daiber, Milo{\v{s}} Stanojevi{\'c}, Khalil Sima{'}an

In this paper we explore the novel idea of building a single universal reordering model from English to a large number of target languages.

Machine Translation Translation

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