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Found 19 papers, 9 papers with code
A Logic-Based Framework for Natural Language Inference in Dutch
Pairs of semantic terms are then fed to an automated theorem prover for natural logic which reasons with them while using the lexical relations found in the Open Dutch WordNet.
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.
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.
Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference
Tackling Natural Language Inference with a logic-based method is becoming less and less common.
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.
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.
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.
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DRS Parsing
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What can we learn from Semantic Tagging?
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging.
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.
LangPro: Natural Language Theorem Prover
LangPro is an automated theorem prover for natural language (https://github. com/kovvalsky/LangPro).
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).
