Robin Cooper


2023

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TTR at the SPA: Relating type-theoretical semantics to neural semantic pointers
Staffan Larsson | Robin Cooper | Jonathan Ginzburg | Andy Luecking
Proceedings of the 4th Natural Logic Meets Machine Learning Workshop

This paper considers how the kind of formal semantic objects used in TTR (a theory of types with records, Cooper 2013) might be related to the vector representations used in Eliasmith (2013). An advantage of doing this is that it would immediately give us a neural representation for TTR objects as Eliasmith relates vectors to neural activity in his semantic pointer architecture (SPA). This would be an alternative using convolution to the suggestions made by Cooper (2019) based on the phasing of neural activity. The project seems potentially hopeful since all complex TTR objects are constructed from labelled sets (essentially sets of ordered pairs consisting of labels and values) which might be seen as corresponding to the representation of structured objects which Eliasmith achieves using superposition and circular convolution.

2022

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Distributional properties of political dogwhistle representations in Swedish BERT
Niclas Hertzberg | Robin Cooper | Elina Lindgren | Björn Rönnerstrand | Gregor Rettenegger | Ellen Breitholtz | Asad Sayeed
Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)

“Dogwhistles” are expressions intended by the speaker have two messages: a socially-unacceptable “in-group” message understood by a subset of listeners, and a benign message intended for the out-group. We take the result of a word-replacement survey of the Swedish population intended to reveal how dogwhistles are understood, and we show that the difficulty of annotating dogwhistles is reflected in the separability in the space of a sentence-transformer Swedish BERT trained on general data.

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In Search of Meaning and Its Representations for Computational Linguistics
Simon Dobnik | Robin Cooper | Adam Ek | Bill Noble | Staffan Larsson | Nikolai Ilinykh | Vladislav Maraev | Vidya Somashekarappa
Proceedings of the 2022 CLASP Conference on (Dis)embodiment

In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the aspects of the natural language meaning they are modelling and in terms of the aspects of the application for which they are used.

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Classification Systems: Combining taxonomical and perceptual lexical meaning
Bill Noble | Staffan Larsson | Robin Cooper
Proceedings of the 3rd Natural Logic Meets Machine Learning Workshop (NALOMA III)

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Fine-grained Entailment: Resources for Greek NLI and Precise Entailment
Eirini Amanaki | Jean-Philippe Bernardy | Stergios Chatzikyriakidis | Robin Cooper | Simon Dobnik | Aram Karimi | Adam Ek | Eirini Chrysovalantou Giannikouri | Vasiliki Katsouli | Ilias Kolokousis | Eirini Chrysovalantou Mamatzaki | Dimitrios Papadakis | Olga Petrova | Erofili Psaltaki | Charikleia Soupiona | Effrosyni Skoulataki | Christina Stefanidou
Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference

In this paper, we present a number of fine-grained resources for Natural Language Inference (NLI). In particular, we present a number of resources and validation methods for Greek NLI and a resource for precise NLI. First, we extend the Greek version of the FraCaS test suite to include examples where the inference is directly linked to the syntactic/morphological properties of Greek. The new resource contains an additional 428 examples, making it in total a dataset of 774 examples. Expert annotators have been used in order to create the additional resource, while extensive validation of the original Greek version of the FraCaS by non-expert and expert subjects is performed. Next, we continue the work initiated by (CITATION), according to which a subset of the RTE problems have been labeled for missing hypotheses and we present a dataset an order of magnitude larger, annotating the whole SuperGlUE/RTE dataset with missing hypotheses. Lastly, we provide a de-dropped version of the Greek XNLI dataset, where the pronouns that are missing due to the pro-drop nature of the language are inserted. We then run some models to see the effect of that insertion and report the results.

2021

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Bayesian Classification and Inference in a Probabilistic Type Theory with Records
Staffan Larsson | Robin Cooper
Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA)

We propose a probabilistic account of semantic inference and classification formulated in terms of probabilistic type theory with records, building on Cooper et. al. (2014) and Cooper et. al. (2015). We suggest probabilistic type theoretic formulations of Naive Bayes Classifiers and Bayesian Networks. A central element of these constructions is a type-theoretic version of a random variable. We illustrate this account with a simple language game combining probabilistic classification of perceptual input with probabilistic (semantic) inference.

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So what’s all this structure good for? Some uses of record types in TTR
Robin Cooper
Proceedings of the ESSLLI 2021 Workshop on Computing Semantics with Types, Frames and Related Structures

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Semantic Learning in a Probabilistic Type Theory with Records
Staffan Larsson | Jean-Philippe Bernardy | Robin Cooper
Proceedings of the ESSLLI 2021 Workshop on Computing Semantics with Types, Frames and Related Structures

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Dogwhistles as Inferences in Interaction
Ellen Breitholtz | Robin Cooper
Proceedings of the Reasoning and Interaction Conference (ReInAct 2021)

In this paper we will argue that the nature of dogwhistle communication is essentially dialogical, and that to account for dogwhistle meaning we must consider dialogical events in which dialogue partners can draw different conclusions based on communicative events. This leads us to a theory based on inference. However, as identified by Khoo (2017) and emphasised by Henderson & McCready (2018), a problematic aspect of this approach is that expressions that have a similar meaning are analysed as generating the same dogwhistle inferences, which appears not always to be the case. By modelling meaning in terms of intensional types in TTR, we avoid this problem.

2020

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Personae under uncertainty: The case of topoi
Bill Noble | Ellen Breitholtz | Robin Cooper
Proceedings of the Probability and Meaning Conference (PaM 2020)

In this paper, we propose a probabilistic model of social signalling which adopts a persona-based account of social meaning. We use this model to develop a socio-semantic theory of conventionalised reasoning patterns, known as topoi. On this account the social meaning of a topos, as conveyed in a argument, is based on the set of idealogically-related topoi it indicates in context. We draw a connection between the role of personae in social meaning and the category adjustment effect, a well-known psychological phenomenon in which the representation of a stimulus is biased in the direction of the category in which it falls. Finally, we situate the interpretation of social signals as an update to the information state of an agent in a formal TTR model of dialogue.

2019

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Proceedings of the Sixth Workshop on Natural Language and Computer Science
Robin Cooper | Valeria de Paiva | Lawrence S. Moss
Proceedings of the Sixth Workshop on Natural Language and Computer Science

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Distribution is not enough: going Firther
Andy Lücking | Robin Cooper | Staffan Larsson | Jonathan Ginzburg
Proceedings of the Sixth Workshop on Natural Language and Computer Science

Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space. In practice, use is identified with occurrence in text corpora, though there are some efforts to use corpora containing multi-modal information. In this paper we argue that the distributional hypothesis is intrinsically misguided as a self-supporting basis for semantics, as Firth was entirely aware. We mention philosophical arguments concerning the lack of normativity within DH data. Furthermore, we point out the shortcomings of DH as a model of learning, by discussing a variety of linguistic classes that cannot be learnt on a distributional basis, including indexicals, proper names, and wh-phrases. Instead of pursuing DH, we sketch an account of the problematic learning cases by integrating a rich, Firthian notion of dialogue context with interactive learning in signalling games backed by in probabilistic Type Theory with Records. We conclude that the success of the DH in computational semantics rests on a post hoc effect: DS presupposes a referential semantics on the basis of which utterances can be produced, comprehended and analysed in the first place.

2017

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Incrementality all the way up
Ellen Breitholtz | Christine Howes | Robin Cooper
Proceedings of the Computing Natural Language Inference Workshop

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An overview of Natural Language Inference Data Collection: The way forward?
Stergios Chatzikyriakidis | Robin Cooper | Simon Dobnik | Staffan Larsson
Proceedings of the Computing Natural Language Inference Workshop

2015

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Probabilistic Type Theory and Natural Language Semantics
Robin Cooper | Simon Dobnik | Shalom Lappin | Staffan Larsson
Linguistic Issues in Language Technology, Volume 10, 2015

Type theory has played an important role in specifying the formal connection between syntactic structure and semantic interpretation within the history of formal semantics. In recent years rich type theories developed for the semantics of programming languages have become influential in the semantics of natural language. The use of probabilistic reasoning to model human learning and cognition has become an increasingly important part of cognitive science. In this paper we offer a probabilistic formulation of a rich type theory, Type Theory with Records (TTR), and we illustrate how this framework can be used to approach the problem of semantic learning. Our probabilistic version of TTR is intended to provide an interface between the cognitive process of classifying situations according to the types that they instantiate, and the compositional semantics of natural language.

2014

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Proceedings of the EACL 2014 Workshop on Type Theory and Natural Language Semantics (TTNLS)
Robin Cooper | Simon Dobnik | Shalom Lappin | Staffan Larsson
Proceedings of the EACL 2014 Workshop on Type Theory and Natural Language Semantics (TTNLS)

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A Probabilistic Rich Type Theory for Semantic Interpretation
Robin Cooper | Simon Dobnik | Shalom Lappin | Staffan Larsson
Proceedings of the EACL 2014 Workshop on Type Theory and Natural Language Semantics (TTNLS)

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Propositions, Questions, and Adjectives: a rich type theoretic approach
Jonathan Ginzburg | Robin Cooper | Tim Fernando
Proceedings of the EACL 2014 Workshop on Type Theory and Natural Language Semantics (TTNLS)

2013

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Spatial Descriptions in Type Theory with Records
Simon Dobnik | Robin Cooper
Proceedings of the IWCS 2013 Workshop on Computational Models of Spatial Language Interpretation and Generation (CoSLI-3)

2012

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Book Review: Computational Semantics with Functional Programming by Jan van Eijck and Christina Unger
Robin Cooper
Computational Linguistics, Volume 38, Issue 2 - June 2012

2009

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Towards a Formal View of Corrective Feedback
Staffan Larsson | Robin Cooper
Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition

2002

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Towards a Web-based Centre on Swedish Language Technology
Petter Karlström | Robin Cooper
COLING-02: The 2nd Workshop on NLP and XML (NLPXML-2002)

2001

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Resolving Ellipsis in Clarification
Jonathan Ginzburg | Robin Cooper
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

2000

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GoDiS- An Accommodating Dialogue System
Staffan Larsson | Peter Ljunglof | Robin Cooper | Elisabet Engdahl | Stina Ericsson
ANLP-NAACL 2000 Workshop: Conversational Systems

1994

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Incremental Interpretation: Applications, Theory, and Relationship to Dynamic Semantics
David Milward | Robin Cooper
COLING 1994 Volume 2: The 15th International Conference on Computational Linguistics