Staffan Larsson


2021

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Semantic Classification and Learning Using a Linear Tranformation Model in a Probabilistic Type Theory with Records
Staffan Larsson | Jean-Philippe Bernardy
Proceedings of the Reasoning and Interaction Conference (ReInAct 2021)

Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with this account and provide an alternative account of classification learning that addresses the observed problems. The proposed account is also broadly Bayesian in nature but instead uses a linear transformation model for classification and learning.

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Semantic shift in social networks
Bill Noble | Asad Sayeed | Raquel Fernández | Staffan Larsson
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Just as the meaning of words is tied to the communities in which they are used, so too is semantic change. But how does lexical semantic change manifest differently across different communities? In this work, we investigate the relationship between community structure and semantic change in 45 communities from the social media website Reddit. We use distributional methods to quantify lexical semantic change and induce a social network on communities, based on interactions between members. We explore the relationship between semantic change and the clustering coefficient of a community’s social network graph, as well as community size and stability. While none of these factors are found to be significant on their own, we report a significant effect of their three-way interaction. We also report on significant word-level effects of frequency and change in frequency, which replicate previous findings.

2020

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Discrete and Probabilistic Classifier-based Semantics
Staffan Larsson
Proceedings of the Probability and Meaning Conference (PaM 2020)

We present a formal semantics (a version of Type Theory with Records) which places classifiers of perceptual information at the core of semantics. Using this framework, we present an account of the interpretation and classification of utterances referring to perceptually available situations (such as visual scenes). The account improves on previous work by clarifying the role of classifiers in a hybrid semantics combining statistical/neural classifiers with logical/inferential aspects of meaning. The account covers both discrete and probabilistic classification, thereby enabling learning, vagueness and other non-discrete linguistic phenomena.

2019

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ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering
Arild Matsson | Simon Dobnik | Staffan Larsson
Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures

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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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User-initiated Sub-dialogues in State-of-the-art Dialogue Systems
Staffan Larsson
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

We test state of the art dialogue systems for their behaviour in response to user-initiated sub-dialogues, i.e. interactions where a system question is responded to with a question or request from the user, who thus initiates a sub-dialogue. We look at sub-dialogues both within a single app (where the sub-dialogue concerns another topic in the original domain) and across apps (where the sub-dialogue concerns a different domain). The overall conclusion of the tests is that none of the systems can be said to deal appropriately with user-initiated sub-dialogues.

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Compositionality for perceptual classification
Staffan Larsson
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

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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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Safe In-vehicle Dialogue Using Learned Predictions of User Utterances
Staffan Larsson | Fredrik Kronlid | Pontus Wärnestål
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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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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Vagueness and Learning: A Type-Theoretic Approach
Raquel Fernández | Staffan Larsson
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

2013

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Integration and test environment for an in-vehicle dialogue system in the SIMSI project
Staffan Larsson | Sebastian Berlin | Anders Eliasson | Fredrik Kronlid
Proceedings of the SIGDIAL 2013 Conference

2011

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Multimodal Menu-based Dialogue with Speech Cursor in DICO II+
Staffan Larsson | Alexander Berman | Jessica Villing
Proceedings of the ACL-HLT 2011 System Demonstrations

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

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TRIK: A Talking and Drawing Robot for Children with Communication Disabilities
Peter Ljunglöf | Staffan Larsson | Katarina Heimann Mühlenbock | Gunilla Thunberg
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)

2002

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Issues under negotiation
Staffan Larsson
Proceedings of the Third SIGdial Workshop on Discourse and Dialogue

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

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Document Transformations and Information States
Staffan Larsson | Annie Zaenen
1st SIGdial Workshop on Discourse and Dialogue