Papers by Sorcha Gilroy

4 papers
Recurrent Neural Networks as Weighted Language Recognizers (N18-1)

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Challenge: Recent experiments show that RNNs outperform other methods in assigning high probability to held-out English text.
Approach: They focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax . they show that most problems for such RNN are undecidable .
Outcome: The proposed model outperforms other methods in assigning high probability to held-out English text.
Graph Formalisms for Meaning Representations (D18-3)

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Challenge: In this tutorial we will discuss hyperedge replacement languages (HRL) HRL are a graph rewriting system that is context-free .
Approach: They will discuss hyperedge replacement languages (HRL) they will also discuss Regular Graph Languages (RGL)
Outcome: This tutorial will discuss hyperedge replacement languages (HRL) they are one of the most popular graph formalisms to be studied in NLP .
The problem with probabilistic DAG automata for semantic graphs (N19-1)

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Challenge: Abstract Meaning Representation (AMR) annotations are directed acyclic graphs, but most probabilistic models view them as strings or trees.
Approach: They show that some DAG automata cannot be made into useful probabilistic models by assigning weights to transitions.
Outcome: The proposed model can't be made into useful probabilistic models by assigning weights to transitions . the proposed model is not feasible for all variants, but it is problematic for planar variants if they are not rooted .
Semantic graph parsing with recurrent neural network DAG grammars (D19-1)

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Challenge: Semantic parsing is the task of mapping natural language to machine interpretable meaning representations.
Approach: They propose a graph-aware sequence model that generates only well-formed graphs . their model is based on a multilingual semantic graphbank .
Outcome: The proposed model yields competitive results in English and establishes the first results for German, Italian and Dutch.

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