Connectionist natural language parsing

The key developments of two decades of connectionist parsing are reviewed. Connectionist parsers are assessed according to their ability to learn to represent syntactic structures from examples automatically, without being presented with symbolic grammar rules. This review also considers the extent to which connectionist parsers offer computational models of human sentence processing and provide plausible accounts of psycholinguistic data. In considering these issues, special attention is paid to the level of realism, the nature of the modularity, and the type of processing that is to be found in a wide range of parsers

Revisiting the Hierarchical Multiscale LSTM

Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art language model that learns interpretable structure from character-level input. Such models can provide fertile ground for (cognitive) computational linguistics studies. However, the high complexity of the architecture, training procedure and implementations might hinder its applicability. We provide a detailed reproduction and ablation study of the architecture, shedding light on some of the potential caveats of re-purposing complex deep-learning architectures. We further show that simplifying certain aspects of the architecture can in fact improve its performance. We also investigate the linguistic units (segments) learned by various levels of the model, and argue that their quality does not correlate with the overall performance of the model on language modeling.Comment: To appear in COLING 2018 (reproduction track

Poverty and social policy in Europe 2020: ungovernable and ungoverned

This is a post-peer-review, pre-copy edited version of an article published in Policy and Politics. The definitive publisher-authenticated version of: Copeland, Paul, and Mary Daly. "Poverty and social policy in Europe 2020: ungovernable and ungoverned." Policy & Politics 42.3 (2014): 351-365 is available online at: http://dx.doi.org/10.1332/030557312X655503This article examines the latest iteration of EU social policy – the target to reduce poverty and social exclusion by 20 million. The application of a three-part conceptual matrix – focused on ideas, politics and governance – indicates key weaknesses. The target is ungovernable because it melds different approaches to poverty and social exclusion and because of the leeway to Member States to adopt an approach of their own choice. And the target is ungoverned because of low political priority and uncertainty around its legal status, as well as ambiguity over how it fits into the Europe 2020 governance process

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Structural Descriptions in Human-Assisted Robot Visual Learning

The paper presents an approach to using structural descriptions, obtained through a human-robot tutoring dialogue, as labels for the visual object models a robot learns. The paper shows how structural descriptions enable relating models for different aspects of one and the same object, and how being able to relate descriptions for visual models and discourse referents enables incremental updating of model descriptions through dialogue (either robot- or human-initiated). The approach has been implemented in an integrated architecture for human-assisted robot visual learning

Non-human Intention and Meaning-Making: An Ecological Theory

© Springer Nature Switzerland AG 2019. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-97550-4_12Social robots have the potential to problematize many attributes that have previously been considered, in philosophical discourse, to be unique to human beings. Thus, if one construes the explicit programming of robots as constituting specific objectives and the overall design and structure of AI as having aims, in the sense of embedded directives, one might conclude that social robots are motivated to fulfil these objectives, and therefore act intentionally towards fulfilling those goals. The purpose of this paper is to consider the impact of this description of social robotics on traditional notions of intention and meaningmaking, and, in particular, to link meaning-making to a social ecology that is being impacted by the presence of social robots. To the extent that intelligent non-human agents are occupying our world alongside us, this paper suggests that there is no benefit in differentiating them from human agents because they are actively changing the context that we share with them, and therefore influencing our meaningmaking like any other agent. This is not suggested as some kind of Turing Test, in which we can no longer differentiate between humans and robots, but rather to observe that the argument in which human agency is defined in terms of free will, motivation, and intention can equally be used as a description of the agency of social robots. Furthermore, all of this occurs within a shared context in which the actions of the human impinge upon the non-human, and vice versa, thereby problematising Anscombe's classic account of intention.Peer reviewedFinal Accepted Versio