Scotland: The new melting pot?

Discussion of population issues increasingly centers on the low fertility rates common in much of the developed world. As a result of these low fertility rates and increased longevity, populations across much of Europe are getting older and in many cases declining in size. In response, governments with low fertility rates have implemented a variety of policies to maintain their population. While many countries have tried to boost fertility, Scotland had opted for a less popular approach, i.e. pro-immigration. This thesis focuses on Scotland and the policy implemented by the government to combat low fertility

Building lightweight semantic search engines

Despite significant advances in methods for processing large volumes of structured and unstructured data, surprisingly little attention has been devoted to developing general practical methodologies that leverage state-of-the-art technologies to build domain-specific semantic search engines tailored to use cases where they could provide substantial benefits. This paper presents a methodology for developing these kinds of systems in a lightweight, modular, and flexible way with a particular focus on providing powerful search tools in domains where non-expert users encounter challenges in exploring the data repository at hand. Using an academic expertise finder tool as a case study, we demonstrate how this methodology allows us to leverage powerful off-the-shelf technology to enable the rapid, low-cost development of semantic search engines, while also affording developers with the necessary flexibility to embed user-centric design in their development in order to maximise uptake and application value.Postprin

Do Language Models Learn about Legal Entity Types during Pretraining?

Language Models (LMs) have proven their ability to acquire diverse linguistic knowledge during the pretraining phase, potentially serving as a valuable source of incidental supervision for downstream tasks. However, there has been limited research conducted on the retrieval of domain-specific knowledge, and specifically legal knowledge. We propose to explore the task of Entity Typing, serving as a proxy for evaluating legal knowledge as an essential aspect of text comprehension, and a foundational task to numerous downstream legal NLP applications. Through systematic evaluation and analysis and two types of prompting (cloze sentences and QA-based templates) and to clarify the nature of these acquired cues, we compare diverse types and lengths of entities both general and domain-specific entities, semantics or syntax signals, and different LM pretraining corpus (generic and legal-oriented) and architectures (encoder BERT-based and decoder-only with Llama2). We show that (1) Llama2 performs well on certain entities and exhibits potential for substantial improvement with optimized prompt templates, (2) law-oriented LMs show inconsistent performance, possibly due to variations in their training corpus, (3) LMs demonstrate the ability to type entities even in the case of multi-token entities, (4) all models struggle with entities belonging to sub-domains of the law (5) Llama2 appears to frequently overlook syntactic cues, a shortcoming less present in BERT-based architectures. The code of the experiments is available at https://github.com/clairebarale/ probing_legal_entity_types

Empirical-Rational Semantics of Agent Communication

The missing of an appropriate semantics of agent communication languages is one of the most challenging issues of contemporary AI. Although several approaches to this problem exist, none of them is really suitable for dealing with agent autonomy, which is a decisive property of artificial agents. This paper introduces an observation-based approach to the semantics of agent communication, which combines benefits of the two most influential traditional approaches to agent communication semantics, namely the mentalistic (agent-centric) and the objectivist (i.e., commitment- or protocol-oriented) approach. Our approach makes use of the fact that the most general meaning of agent utterances lays in their expectable consequences in terms of agent actions, and that communications result from hidden but nevertheless rational and to some extent reliable agent intentions. In this work, we present a formal framework which enables the empirical derivation of communication meanings from the observation of rational agent utterances, and introduce thereby a probabilistic and utility-oriented perspective of social commitments

Automated Refugee Case Analysis: An NLP Pipeline for Supporting Legal Practitioners

In this paper, we introduce an end-to-end pipeline for retrieving, processing, and extracting targeted information from legal cases. We investigate an under-studied legal domain with a case study on refugee law in Canada. Searching case law for past similar cases is a key part of legal work for both lawyers and judges, the potential end-users of our prototype. While traditional named-entity recognition labels such as dates provide meaningful information in legal work, we propose to extend existing models and retrieve a total of 19 useful categories of items from refugee cases. After creating a novel data set of cases, we perform information extraction based on state-of-the-art neural named-entity recognition (NER). We test different architectures including two transformer models, using contextual and non-contextual embeddings, and compare general purpose versus domain-specific pre-training. The results demonstrate that models pre-trained on legal data perform best despite their smaller size, suggesting that domain matching had a larger effect than network architecture. We achieve a F1 score above 90% on five of the targeted categories and over 80% on four further categories.Comment: 9 pages, preprint of long paper accepted to Findings of the Annual Meeting of the Association for Computational Linguistics (ACL) 202

Multiagent Systems for Social Computation

This paper proposes social computation, i.e. large-scale man-machine collaboration mediated by digital interaction me-dia, as a vision for future intelligent systems, and as a new challenge for multiagent systems research. We claim that the study of social computation suggests a re-interpretation of many traditional AI endeavours, has huge potential appli-cation benefits, and presents the field of multiagent systems with novel, exciting research questions. We introduce an ab-stract model of social computation that helps capture some of its core research problems more precisely. We explore the potential contribution of multiagent systems technologies to the solution of these problems by exposing the close relation-ship between social computation and existing methods in multiagent systems. We describe how these methods could be reused in this novel application context, what method-ological implications this has, and argue that the resulting cross-fertilisation will be highly beneficial for both sides