Search Results for author: Johan Bos

Found 51 papers, 14 papers with code

Universal Discourse Representation Structure Parsing

no code implementations CL (ACL) 2021 Jiangming Liu, Shay B. Cohen, Mirella Lapata, Johan Bos

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.

Semantic Parsing

Evaluating Text Generation from Discourse Representation Structures

1 code implementation ACL (GEM) 2021 Chunliu Wang, Rik van Noord, Arianna Bisazza, Johan Bos

We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs).

Text Generation

Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations

1 code implementation COLING 2022 Wessel Poelman, Rik van Noord, Johan Bos

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.

Semantic Parsing

Quantification Annotation in ISO 24617-12, Second Draft

no code implementations LREC 2022 Harry Bunt, Maxime Amblard, Johan Bos, Karën Fort, Bruno Guillaume, Philippe de Groote, Chuyuan Li, Pierre Ludmann, Michel Musiol, Siyana Pavlova, Guy Perrier, Sylvain Pogodalla

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.

Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation

1 code implementation31 May 2023 Chunliu Wang, Huiyuan Lai, Malvina Nissim, Johan Bos

Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics.

Cross-Lingual Transfer DRS Parsing +2

What's the Meaning of Superhuman Performance in Today's NLU?

no code implementations15 May 2023 Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajic, Daniel Hershcovich, Eduard H. Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, Roberto Navigli

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.

Reading Comprehension

DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs

1 code implementation CONLL 2020 Lasha Abzianidze, Johan Bos, Stephan Oepen

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

no code implementations29 Dec 2020 Lasha Abzianidze, Rik van Noord, Chunliu Wang, Johan Bos

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.

Word Sense Disambiguation

Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

2 code implementations EMNLP 2020 Rik van Noord, Antonio Toral, Johan Bos

We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing.

DRS Parsing Language Modelling

MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

no code implementations CONLL 2020 Stephan Oepen, Omri Abend, Lasha Abzianidze, Johan Bos, Jan Hajic, Daniel Hershcovich, Bin Li, Tim O{'}Gorman, Nianwen Xue, Daniel Zeman

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

no code implementations WS 2019 Johan Bos, Lasha Abzianidze

Meaning banking--creating a semantically annotated corpus for the purpose of semantic parsing or generation--is a challenging task.

Semantic Parsing

The First Shared Task on Discourse Representation Structure Parsing

no code implementations WS 2019 Lasha Abzianidze, Rik van Noord, Hessel Haagsma, Johan Bos

To measure similarity between two DRSs, they are represented in a clausal form, i. e. as a set of tuples.

DRS Parsing

MAGPIE: A Large Corpus of Potentially Idiomatic Expressions

no code implementations LREC 2020 Hessel Haagsma, Johan Bos, Malvina Nissim

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

no code implementations20 Nov 2019 Hessel Haagsma, Malvina Nissim, Johan Bos

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

no code implementations DMR (COLING) 2020 Johan Bos

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

no code implementations WS 2019 Kilian Evang, Lasha Abzianidze, Johan Bos

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?

1 code implementation WS 2019 Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos

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.

Data Augmentation Natural Language Inference

Exploring Neural Methods for Parsing Discourse Representation Structures

1 code implementation TACL 2018 Rik van Noord, Lasha Abzianidze, Antonio Toral, Johan Bos

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.

DRS Parsing

The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses

no code implementations COLING 2018 Hessel Haagsma, Malvina Nissim, Johan Bos

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.

Machine Translation Sentiment Analysis +1

Evaluating Scoped Meaning Representations

2 code implementations LREC 2018 Rik van Noord, Lasha Abzianidze, Hessel Haagsma, Johan Bos

A pilot study is performed to automatically find changes in meaning by comparing meaning representations of translations.

Natural Language Understanding Semantic Parsing

Towards Universal Semantic Tagging

no code implementations WS 2017 Lasha Abzianidze, Johan Bos

The paper proposes the task of universal semantic tagging---tagging word tokens with language-neutral, semantically informative tags.

Semantic Parsing

Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations

2 code implementations28 May 2017 Rik van Noord, Johan Bos

We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs).

AMR Parsing Translation

The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing

no code implementations SEMEVAL 2017 Rik van Noord, Johan Bos

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.

Data Augmentation POS +1

Meaning Banking beyond Events and Roles

no code implementations WS 2017 Johan Bos

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

1 code implementation EACL 2017 Lasha Abzianidze, Johannes Bjerva, Kilian Evang, Hessel Haagsma, Rik van Noord, Pierre Ludmann, Duc-Duy Nguyen, Johan Bos

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

no code implementations COLING 2016 Kilian Evang, Johan Bos

We propose a method for learning semantic CCG parsers by projecting annotations via a parallel corpus.

Semantic Parsing

Semantic Tagging with Deep Residual Networks

1 code implementation COLING 2016 Johannes Bjerva, Barbara Plank, Johan Bos

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).

Part-Of-Speech Tagging POS +1

Developing a large semantically annotated corpus

no code implementations LREC 2012 Valerio Basile, Johan Bos, Kilian Evang, Noortje Venhuizen

What would be a good method to provide a large collection of semantically annotated texts with formal, deep semantics rather than shallow?

Boundary Detection Part-Of-Speech Tagging

Cannot find the paper you are looking for? You can Submit a new open access paper.