Florin Macicasan, Alexandru Frasie, Nicoleta-Teodora Vezan, Camelia Lemnaru, Rodica Potolea
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Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks.
Meaning Representation parsing aims to represent a sentence as a structured, Directed, Acyclic Graph (DAG), in an attempt to extract meaning from text. This paper extends an existing 2-stage pipeline AMR parser with state-of-the-art techniques in dependency parsing. First, Pointer-Generator Networks are used for out-of-vocabulary words in the concept identification stage, with an improved initialization via the use of word-and character-level embeddings. Second, the performance of the Relation Identification module is improved by jointly training the Heads Selection and the Arcs Labeling components. Last, we underline the difficulty of end-to-end training with recurrent modules in a static deep neural network construction approach and explore a dynamic construction implementation, which continuously adapts the computation graph, thus potentially enabling end-to-end training in the proposed pipeline solution.
期刊介绍:
The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.