Now showing items 1-9 of 9

    • A Unifying Theory of Transition-based and Sequence Labeling Parsing 

      Gómez-Rodríguez, Carlos; Strzyz, Michalina; Vilares, David (International Committee on Computational Linguistics, 2020-12)
      [Absctract]: We define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a theoretical relation between ...
    • Bracketing Encodings for 2-Planar Dependency Parsing 

      Strzyz, Michalina; Vilares, David; Gómez-Rodríguez, Carlos (International Committee on Computational Linguistics, 2020-12)
      [Absctract]: We present a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels, hence providing almost total coverage of crossing arcs ...
    • Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing 

      Muñoz-Ortiz, Alberto; Strzyz, Michalina; Vilares, David (INCOMA Ltd., 2021-09)
      [Absctract]: Different linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of the token arcs as bracket ...
    • Parsing as Pretraining 

      Vilares, David; Strzyz, Michalina; Søgaard, Anders; Gómez-Rodríguez, Carlos (2020)
      [Abstract] Recent analyses suggest that encoders pretrained for language modeling capture certain morpho-syntactic structure. However, probing frameworks for word vectors still do not report results on standard setups ...
    • Sequence Labeling Parsing by Learning across Representations 

      Strzyz, Michalina; Vilares, David; Gómez-Rodríguez, Carlos (Association for Computational Linguistics, 2019-07)
      [Absctract]: We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, we show that ...
    • Sequence Tagging for Fast Dependency Parsing 

      Strzyz, Michalina; Vilares, David; Gómez-Rodríguez, Carlos (2019)
      [Abstract] Dependency parsing has been built upon the idea of using parsing methods based on shift-reduce or graph-based algorithms in order to identify binary dependency relations between the words in a sentence. In this ...
    • Towards Making a Dependency Parser See 

      Strzyz, Michalina; Vilares, David; Gómez-Rodríguez, Carlos (Association for Computational Linguistics, 2019-11)
      [Absctract]: We explore whether it is possible to leverage eye-tracking data in an RNN dependency parser (for English) when such information is only available during training - i.e. no aggregated or token-level gaze features ...
    • Viability of Sequence Labeling Encodings for Dependency Parsing 

      Strzyz, Michalina (2021)
      [Abstract] This thesis presents new methods for recasting dependency parsing as a sequence labeling task yielding a viable alternative to the traditional transition- and graph-based approaches. It is shown that sequence ...
    • Viable Dependency Parsing as Sequence Labeling 

      Strzyz, Michalina; Vilares, David; Gómez-Rodríguez, Carlos (Association for Computational Linguistics (ACL), 2019-06)
      [Abstract]: We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, ...