Johan Bos


2019

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HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Hitomi Yanaka | Koji Mineshima | Daisuke Bekki | Kentaro Inui | Satoshi Sekine | Lasha Abzianidze | Johan Bos

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 capturing logical inferences, including those licensed by phrase replacements, so-called monotonicity reasoning. Since no large dataset has been developed for monotonicity reasoning, it is still unclear whether the main obstacle is the size of datasets or the model architectures themselves. To investigate this issue, we introduce a new dataset, called HELP, for handling entailments with lexical and logical phenomena. We add it to training data for the state-of-the-art neural models and evaluate them on test sets for monotonicity phenomena. The results showed that our data augmentation improved the overall accuracy. We also find that the improvement is better on monotonicity inferences with lexical replacements than on downward inferences with disjunction and modification. This suggests that some types of inferences can be improved by our data augmentation while others are immune to it.

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Linguistic Information in Neural Semantic Parsing with Multiple Encoders
Rik van Noord | Antonio Toral | Johan Bos

Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.

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Proceedings of the IWCS Shared Task on Semantic Parsing
Lasha Abzianidze | Rik van Noord | Hessel Haagsma | Johan Bos

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The First Shared Task on Discourse Representation Structure Parsing
Lasha Abzianidze | Rik van Noord | Hessel Haagsma | Johan Bos

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 Discourse Representation Theory and represent scoped meaning representations that capture the semantics of negation, modals, quantification, and presupposition triggers. Additionally, concepts and event-participants in DRSs are described with WordNet synsets and the thematic roles from VerbNet. To measure similarity between two DRSs, they are represented in a clausal form, i.e. as a set of tuples. Participant systems were expected to produce DRSs in this clausal form. Taking into account the rich lexical information, explicit scope marking, a high number of shared variables among clauses, and highly-constrained format of valid DRSs, all these makes the DRS parsing a challenging NLP task. The results of the shared task displayed improvements over the existing state-of-the-art parser.

2018

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What can we learn from Semantic Tagging?
Mostafa Abdou | Artur Kulmizev | Vinit Ravishankar | Lasha Abzianidze | Johan Bos

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 tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting which shows consistent gains across all tasks.

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Exploring Neural Methods for Parsing Discourse Representation Structures
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. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn indices increases parser performance. Adding silver training data boosts performance even further.

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Evaluating Scoped Meaning Representations
Rik van Noord | Lasha Abzianidze | Hessel Haagsma | Johan Bos

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The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses
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.’ is used in a figurative sense. This enables automatic interpretation of idiomatic expressions, which is important for applications like machine translation and sentiment analysis. In this work, we present an unsupervised approach for English that makes use of literalisations of idiom senses to improve disambiguation, which is based on the lexical cohesion graph-based method by Sporleder and Li (2009). Experimental results show that, while literalisation carries novel information, its performance falls short of that of state-of-the-art unsupervised methods.

2017

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Meaning Banking beyond Events and Roles
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. I will do this in a version of Discourse Representation Theory, using a universal semantic tagset developed as part of a project that aims to produce a large meaning bank (a semantically-annotated corpus) for four languages (English, Dutch, German and Italian).

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Towards Universal Semantic Tagging
Lasha Abzianidze | Johan Bos

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Indexicals and Compositionality: Inside-Out or Outside-In?
Johan Bos

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Dealing with Co-reference in Neural Semantic Parsing
Rik van Noord | Johan Bos

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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 augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based neural semantic parsing models, the overall accuracy is still lower than a state-of-the-art AMR parser. An ensemble combining our neural semantic parser with an existing, traditional parser, yields a small gain in performance.

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The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
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). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations, assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) segmentation of the text in sentences and lexical items; (ii) syntactic parsing with Combinatory Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semi-supervised manner. The employed annotation models are all language-neutral. Our first results are promising.

2016

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Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images
Manuela Hürlimann | Johan Bos

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Squib: Expressive Power of Abstract Meaning Representations
Johan Bos

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The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer
Johannes Bjerva | Johan Bos | Hessel Haagsma

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Cross-lingual Learning of an Open-domain Semantic Parser
Kilian Evang | Johan Bos

We propose a method for learning semantic CCG parsers by projecting annotations via a parallel corpus. The method opens an avenue towards cheaply creating multilingual semantic parsers mapping open-domain text to formal meaning representations. A first cross-lingually learned Dutch (from English) semantic parser obtains f-scores ranging from 42.99% to 69.22% depending on the level of label informativity taken into account, compared to 58.40% to 78.88% for the underlying source-language system. These are promising numbers compared to state-of-the-art semantic parsing in open domains.

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Semantic Tagging with Deep Residual Networks
Johannes Bjerva | Barbara Plank | Johan Bos

We propose a novel semantic tagging task, semtagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both word and character representations, and includes a novel residual bypass architecture. We evaluate the tagset both intrinsically on the new task of semantic tagging, as well as on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an auxiliary loss function predicting our semantic tags, significantly outperforms prior results on English Universal Dependencies POS tagging (95.71% accuracy on UD v1.2 and 95.67% accuracy on UD v1.3).

2015

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Adding Semantics to Data-Driven Paraphrasing
Ellie Pavlick | Johan Bos | Malvina Nissim | Charley Beller | Benjamin Van Durme | Chris Callison-Burch

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Uncovering Noun-Noun Compound Relations by Gamification
Johan Bos | Malvina Nissim

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Open-Domain Semantic Parsing with Boxer
Johan Bos

2014

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Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)
Johan Bos | Anette Frank | Roberto Navigli

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RoBox: CCG with Structured Perceptron for Supervised Semantic Parsing of Robotic Spatial Commands
Kilian Evang | Johan Bos

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The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity
Johannes Bjerva | Johan Bos | Rob van der Goot | Malvina Nissim

2013

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Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Tutorials)
Johan Bos | Keith Hall

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Parsimonious Semantic Representations with Projection Pointers
Noortje J. Venhuizen | Johan Bos | Harm Brouwer

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Scope Disambiguation as a Tagging Task
Kilian Evang | Johan Bos

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Gamification for Word Sense Labeling
Noortje J. Venhuizen | Valerio Basile | Kilian Evang | Johan Bos

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Aligning Formal Meaning Representations with Surface Strings for Wide-Coverage Text Generation
Valerio Basile | Johan Bos

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The Groningen Meaning Bank
Johan Bos

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Elephant: Sequence Labeling for Word and Sentence Segmentation
Kilian Evang | Valerio Basile | Grzegorz Chrupała | Johan Bos

2012

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*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)
Eneko Agirre | Johan Bos | Mona Diab | Suresh Manandhar | Yuval Marton | Deniz Yuret

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UGroningen: Negation detection with Discourse Representation Structures
Valerio Basile | Johan Bos | Kilian Evang | Noortje Venhuizen

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Developing a large semantically annotated corpus
Valerio Basile | Johan Bos | Kilian Evang | Noortje Venhuizen

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Predicting the 2011 Dutch Senate Election Results with Twitter
Erik Tjong Kim Sang | Johan Bos

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A platform for collaborative semantic annotation
Valerio Basile | Johan Bos | Kilian Evang | Noortje Venhuizen

2011

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Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)
Johan Bos | Stephen Pulman

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Towards Generating Text from Discourse Representation Structures
Valerio Basile | Johan Bos

2010

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Rebanking CCGbank for Improved NP Interpretation
Matthew Honnibal | James R. Curran | Johan Bos

2009

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Computing Genitive Superlatives
Johan Bos

2008

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Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Johan Bos | Edward Briscoe | Aoife Cahill | John Carroll | Stephen Clark | Ann Copestake | Dan Flickinger | Josef van Genabith | Julia Hockenmaier | Aravind Joshi | Ronald Kaplan | Tracy Holloway King | Sandra Kuebler | Dekang Lin | Jan Tore Lønning | Christopher Manning | Yusuke Miyao | Joakim Nivre | Stephan Oepen | Kenji Sagae | Nianwen Xue | Yi Zhang

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Semantics in Text Processing. STEP 2008 Conference Proceedings
Johan Bos | Rodolfo Delmonte

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Introduction to the Shared Task on Comparing Semantic Representations
Johan Bos

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Wide-Coverage Semantic Analysis with Boxer
Johan Bos

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Let’s not Argue about Semantics
Johan Bos

2007

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Linguistically Motivated Large-Scale NLP with C&C and Boxer
James Curran | Stephen Clark | Johan Bos

2006

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An Empirical Approach to the Interpretation of Superlatives
Johan Bos | Malvina Nissim

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Proceedings of the Fifth International Workshop on Inference in Computational Semantics (ICoS-5)
Johan Bos | Alexander Koller

2005

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Recognising Textual Entailment with Logical Inference
Johan Bos | Katja Markert

2004

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Unificational Combinatory Categorial Grammar. Combining Information Structure and Discourse Representations
Maarika Traat | Johan Bos

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Wide-Coverage Semantic Representations from a CCG Parser
Johan Bos | Stephen Clark | Mark Steedman | James R. Curran | Julia Hockenmaier

2003

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DIPPER: Description and Formalisation of an Information-State Update Dialogue System Architecture
Johan Bos | Ewan Klein | Oliver Lemon | Tetsushi Oka

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Automatic Multi-Layer Corpus Annotation for Evaluation Question Answering Methods: CBC4Kids
Jochen L. Leidner | Tiphaine Dalmas | Bonnie Webber | Johan Bos | Claire Grover

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Implementing the Binding and Accommodation Theory for Anaphora Resolution and Presupposition Projection
Johan Bos

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Meaningful Conversation with a Mobile Robot
Johan Bos | Ewan Klein | Tetsushi Oka

2002

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An Inference-based Approach to Dialogue System Design
Johan Bos | Tetsushi Oka

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Compilation of Unification Grammars with Compositional Semantics to Speech Recognition Packages
Johan Bos

1998

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Managing Information at Linguistic Interfaces
Johan Bos | C.J. Rupp | Bianka Buschbeck-Wolf | Michael Dorna

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Semantic-Head Based Resolution of Scopal Ambiguities
Bjorn Gamback | Johan Bos

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Managing information at linguistic interfaces
Johan Bos | C.J. Rupp | Bianka Buschbeck-Wolf | Michael Dorna

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Semantic-Head Based Resolution of Scopal Ambiguities
Bjorn Gamback | Johan Bos

1996

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Compositional Semantics in Verbmobil
Johan Bos | Bjorn Gamback | Christian Lieske | Yoshiki Mori | Manfred Pinkal | Karsten Worm

1994

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PRESUPPOSITION & VP-ELLIPSIS
Johan Bos