80 citations · 234 across the 21 of their papers we have counts for
20 papers · 1 filter
Discourse Representation Structure Parsing for Chinese
Chunliu Wang, Xiao Zhang, Johan Bos
Previous work has predominantly focused on monolingual English semantic parsing. We, instead, explore the feasibility of Chinese semantic parsing in the absence of labeled data for…
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
Chunliu Wang, Huiyuan Lai, Malvina Nissim +1
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit fr…
What's the Meaning of Superhuman Performance in Today's NLU?
Simone Tedeschi, Johan Bos, Thierry Declerck +9
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 su…
The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages
Lasha Abzianidze, Rik van Noord, Chunliu Wang +1
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…
DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs
Lasha Abzianidze, Johan Bos, Stephan Oepen
Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse. Meaning in DRT is modeled via a Discourse Representation Struc…
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT
Rik van Noord, Antonio Toral, Johan Bos
We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily…
Thirty Musts for Meaning Banking
Johan Bos, Lasha Abzianidze
Meaning banking--creating a semantically annotated corpus for the purpose of semantic parsing or generation--is a challenging task. It is quite simple to come up with a complex mea…
The First Shared Task on Discourse Representation Structure Parsing
Lasha Abzianidze, Rik van Noord, Hessel Haagsma +1
The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from…
Casting a Wide Net: Robust Extraction of Potentially Idiomatic Expressions
Hessel Haagsma, Malvina Nissim, Johan Bos
Idiomatic expressions like `out of the woods' and `up the ante' present a range of difficulties for natural language processing applications. We present work on the annotation and…
Separating Argument Structure from Logical Structure in AMR
Johan Bos
The AMR (Abstract Meaning Representation) formalism for representing meaning of natural language sentences was not designed to deal with scope and quantifiers. By extending AMR wit…
Can neural networks understand monotonicity reasoning?
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
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 interactio…
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neural models have a hard time cap…
Exploring Neural Methods for Parsing Discourse Representation Structures
Rik van Noord, Lasha Abzianidze, Antonio Toral +1
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. We…
What can we learn from Semantic Tagging?
Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar +2
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP ta…
Evaluating Scoped Meaning Representations
Rik van Noord, Lasha Abzianidze, Hessel Haagsma +1
Semantic parsing offers many opportunities to improve natural language understanding. We present a semantically annotated parallel corpus for English, German, Italian, and Dutch wh…
Towards Universal Semantic Tagging
Lasha Abzianidze, Johan Bos
The paper proposes the task of universal semantic tagging---tagging word tokens with language-neutral, semantically informative tags. We argue that the task, with its independent n…
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
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). Using a sequen…
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
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. With data augmentat…
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
Lasha Abzianidze, Johannes Bjerva, Kilian Evang +5
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, G…
Semantic Tagging with Deep Residual Networks
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…