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Found 56 papers, 15 papers with code
Universal Discourse Representation Structure Parsing
Abstract We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages.
Evaluating Text Generation from Discourse Representation Structures
We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs).
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations
Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and explainability.
Quantification Annotation in ISO 24617-12, Second Draft
This paper describes the continuation of a project that aims at establishing an interoperable annotation schema for quantification phenomena as part of the ISO suite of standards for semantic annotation, known as the Semantic Annotation Framework.
Scope-enhanced Compositional Semantic Parsing for DRT
Discourse Representation Theory (DRT) distinguishes itself from other semantic representation frameworks by its ability to model complex semantic and discourse phenomena through structural nesting and variable binding.
Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations
We introduce a neural "taxonomical" semantic parser to utilize this new representation system of predicates, and compare it with a standard neural semantic parser trained on the traditional meaning representation format, employing a novel challenge set and evaluation metric for evaluation.
Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks
The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation.
Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networks
The idea is to add pragmatic information such as topic to the meaning representation, thereby forcing either active or passive voice when given to a natural language generation system.
Discourse Representation Structure Parsing for Chinese
Previous work has predominantly focused on monolingual English semantic parsing.
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics.
What's the Meaning of Superhuman Performance in Today's NLU?
In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension.
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese
Even with DRSs based on English, good results for Chinese are obtained.
DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs
Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse.
The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages
This paper gives a general description of the ideas behind the Parallel Meaning Bank, a framework with the aim to provide an easy way to annotate compositional semantics for texts written in languages other than English.
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT
We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing.
Ranked #1 on
DRS Parsing
on PMB-2.2.0
MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework.
Thirty Musts for Meaning Banking
Meaning banking--creating a semantically annotated corpus for the purpose of semantic parsing or generation--is a challenging task.
The First Shared Task on Discourse Representation Structure Parsing
To measure similarity between two DRSs, they are represented in a clausal form, i. e. as a set of tuples.
MAGPIE: A Large Corpus of Potentially Idiomatic Expressions
Given the limited size of existing idiom corpora, we aim to enable progress in automatic idiom processing and linguistic analysis by creating the largest-to-date corpus of idioms for English.
Casting a Wide Net: Robust Extraction of Potentially Idiomatic Expressions
To further progress on the extraction and disambiguation of potentially idiomatic expressions, larger corpora of PIEs are required.
Separating Argument Structure from Logical Structure in AMR
The AMR (Abstract Meaning Representation) formalism for representing meaning of natural language sentences was not designed to deal with scope and quantifiers.
CCGweb: a New Annotation Tool and a First Quadrilingual CCG Treebank
We present the first open-source graphical annotation tool for combinatory categorial grammar (CCG), and the first set of detailed guidelines for syntactic annotation with CCG, for four languages: English, German, Italian, and Dutch.
Can neural networks understand monotonicity reasoning?
Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures.
Linguistic Information in Neural Semantic Parsing with Multiple Encoders
Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks.
Ranked #2 on
DRS Parsing
on PMB-3.0.0
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
To investigate this issue, we introduce a new dataset, called HELP, for handling entailments with lexical and logical phenomena.
Exploring Neural Methods for Parsing Discourse Representation Structures
Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics.
Ranked #3 on
DRS Parsing
on PMB-3.0.0
What can we learn from Semantic Tagging?
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging.
The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses
Disambiguation of potentially idiomatic expressions involves determining the sense of a potentially idiomatic expression in a given context, e. g. determining that make hay in {`}Investment banks made hay while takeovers shone.
Evaluating Scoped Meaning Representations
A pilot study is performed to automatically find changes in meaning by comparing meaning representations of translations.
Towards Universal Semantic Tagging
The paper proposes the task of universal semantic tagging---tagging word tokens with language-neutral, semantically informative tags.
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs).
Ranked #26 on
AMR Parsing
on LDC2017T10
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs.
Meaning Banking beyond Events and Roles
In this talk I will discuss the analysis of several semantic phenomena that need meaning representations that can describe attributes of propositional contexts.
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch).
Cross-lingual Learning of an Open-domain Semantic Parser
We propose a method for learning semantic CCG parsers by projecting annotations via a parallel corpus.
Semantic Tagging with Deep Residual Networks
We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets).
Developing a large semantically annotated corpus
What would be a good method to provide a large collection of semantically annotated texts with formal, deep semantics rather than shallow?
