scispace - formally typeset
Open AccessProceedings Article

Smatch: an Evaluation Metric for Semantic Feature Structures

Reads0
Chats0
TLDR
This paper presents smatch, a metric that calculates the degree of overlap between two semantic feature structures, and gives an efficient algorithm to compute the metric and shows the results of an inter-annotator agreement study.
Abstract
The evaluation of whole-sentence semantic structures plays an important role in semantic parsing and large-scale semantic structure annotation. However, there is no widely-used metric to evaluate wholesentence semantic structures. In this paper, we present smatch, a metric that calculates the degree of overlap between two semantic feature structures. We give an efficient algorithm to compute the metric and show the results of an inter-annotator agreement study.

read more

Content maybe subject to copyright    Report

Citations
More filters
Proceedings Article

Dialogue-AMR: Abstract Meaning Representation for Dialogue.

TL;DR: A schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems is described and an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect is presented.
Proceedings ArticleDOI

Discourse Representation Structure Parsing

TL;DR: An open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT) and develops a structure-aware model which decomposes the decoding process into three stages.
Proceedings ArticleDOI

Better Transition-Based AMR Parsing with a Refined Search Space

TL;DR: This paper proposes to conduct the search in a refined search space based on a new compact AMR graph and an improved oracle, and achieves the state-of-the-art performance on various datasets with minimal additional information.
Posted Content

ARSENAL: Automatic Requirements Specification Extraction from Natural Language

TL;DR: ARSENAL is a framework and methodology for systematically transforming natural language NL requirements into analyzable formal models and logic specifications that can be analyzed automatically for consistency and implementability.
Proceedings ArticleDOI

Improving AMR Parsing with Sequence-to-Sequence Pre-training

TL;DR: This paper proposes a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself, and extends the vanilla fine-tuning method to a multi-task learning fine- Tuning method that optimizes for the performance of AMR parse while endeavors to preserve the response of pre- trained models.
References
More filters
Proceedings ArticleDOI

Bleu: a Method for Automatic Evaluation of Machine Translation

TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Proceedings Article

A Study of Translation Edit Rate with Targeted Human Annotation

TL;DR: A new, intuitive measure for evaluating machine translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments is defined.
Book

Events in the Semantics of English: A Study in Subatomic Semantics

TL;DR: Focusing on the structure of meaning in English sentences at a "subatomic" level - that is, a level below the one most theories accept as basic or "atomic" - Parsons asserts that the semantics of simple English sentences require logical forms somewhat more complex than is normally assumed in natural language semantics.
Proceedings Article

Learning to map sentences to logical form: structured classification with probabilistic categorial grammars

TL;DR: A learning algorithm is described that takes as input a training set of sentences labeled with expressions in the lambda calculus and induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence.
Proceedings Article

From treebank to propbank

TL;DR: This paper describes the approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank and introduces metaframes as a technique for handling similar frames among near− synonymous verbs.
Related Papers (5)