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Universal Conceptual Cognitive Annotation (UCCA)
UCCA is presented, a novel multi-layered framework for semantic representation that aims to accommodate the semantic distinctions expressed through linguistic utterances and its relative insensitivity to meaning-preserving syntactic variation is demonstrated.
Learnability-Based Syntactic Annotation Design
This work presents a methodology for syntactic selection and applies it to six central dependency structures, comparing pairs of annotation schemes that differ in the annotation of a single structure and finds that in three of the structures, one annotation is unequivocally better than the alternatives.
BLEU is Not Suitable for the Evaluation of Text Simplification
This paper manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments that found low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved.
A Transition-Based Directed Acyclic Graph Parser for UCCA
This work presents the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation and its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.
Simple and Effective Text Simplification Using Semantic and Neural Methods
This work presents a simple and efficient splitting algorithm based on an automatic semantic parser that compares favorably to the state-of-the-art in combined lexical and structural simplification.
Semantic Structural Evaluation for Text Simplification
This paper proposes the first measure to address structural aspects of text simplification, called SAMSA, which leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output.
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
The SemEval 2019 shared task on Universal Conceptual Cognitive Annotation parsing in English, German and French is presented, and the participating systems and results are discussed.
Multitask Parsing Across Semantic Representations
It is shown that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings, and uniform transition-based system and learning architecture for all parsing tasks are experiment on.
MRP 2019: Cross-Framework Meaning Representation Parsing
The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the
Improved Unsupervised POS Induction through Prototype Discovery
We present a novel fully unsupervised algorithm for POS induction from plain text, motivated by the cognitive notion of prototypes. The algorithm first identifies landmark clusters of words, serving