AMR Dependency Parsing with a Typed Semantic Algebra

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see https://acl2018.org/programme/papers/

Sponsoring, brand value and social media

The increasing involvement of individuals in social media over the past decade has enabled firms to pursue new avenues in communication and sponsoring activities. Besides general research on either social media or sponsoring, questions regarding the consequences of a joint activity (sponsoring activities in social media) remain unexplored. Hence, the present study analyses whether the perceived image of the brand and the celebrity endorser credibility of a top sports team influence the perceived brand value of the sponsoring firm in a social media setting. Moreover, these effects are compared between existing customers and non-customers of the sponsoring firm. Interestingly, perceived celebrity endorser credibility plays no role in forming brand value perceptions in the case of the existing customers. Implications for marketing theory and practice are derived. (authors' abstract

Fast semantic parsing with well-typedness guarantees

AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.Comment: Accepted at EMNLP 2020, camera-ready versio

Compositional Generalisation with Structured Reordering and Fertility Layers

Seq2seq models have been shown to struggle with compositional generalisation, i.e. generalising to new and potentially more complex structures than seen during training. Taking inspiration from grammar-based models that excel at compositional generalisation, we present a flexible end-to-end differentiable neural model that composes two structural operations: a fertility step, which we introduce in this work, and a reordering step based on previous work (Wang et al., 2021). Our model outperforms seq2seq models by a wide margin on challenging compositional splits of realistic semantic parsing tasks that require generalisation to longer examples. It also compares favourably to other models targeting compositional generalisation

PPC Task Plan Sourcing - Synchronization of Procurement and Production. A Model-based Observation

As companies continue to globalize, manufacturers face the challenge of strategically adjusting their vertical integration and restructuring production and supply chains. This leads manufacturers to increasingly pursue two strategies of restructuring. On the one hand, in the form of outsourcing value-adding activities, the focus is being placed on the core competencies of the company's own production. As a result, the vertical range of manufacture within the company is decreasing, while outsourcing is becoming more and more important. On the other hand, companies are also pursuing the strategy of least possible dependency to secure production through regional procurement of resources and expansion of the necessary competencies by means of increased vertical integration. In order to understand the consequences and effects of these changes at the level of production planning and control (PPC), a model-based view is necessary for an expanded understanding of the processual context of these changes. The PPC is the essential steering instance of production. It combines long-term tasks, e.g. Plan Sales or Roughly Plan Resources, with short-term tasks, e.g. Schedule Throughput or Plan Resources in Detail. The main PPC task, Plan Sourcing, is an essential link with its tasks and procedures between the core processes of procurement and production in the company's internal supply chain. In the context of this paper, the PPC main task Plan Sourcing is to be considered in a model-based manner, which focuses on the selection and connection of suppliers as well as the general view of the supplier management of manufacturers. For this purpose, the effect on the PPC and the production logistic objectives variables is presented by means of the consideration of the tasks and possible procedures for the fulfilment of these PPC tasks. Utilizing collected findings, a process-related derivation for the synchronization of the affected areas of procurement and production is presented

Perturbed angular correlations for Gd in gadolinium: in-beam comparisons of relative magnetizations

Perturbed angular correlations were measured for Gd ions implanted into gadolinium foils following Coulomb excitation with 40 MeV O-16 beams. A technique for measuring the relative magnetizations of ferromagnetic gadolinium hosts under in-beam conditions is described and discussed. The combined electric-quadrupole and magnetic-dipole interaction is evaluated. The effect of nuclei implanted onto damaged or non-substitutional sites is assessed, as is the effect of misalignment between the internal hyperfine field and the external polarizing field. Thermal effects due to beam heating are discussed.Comment: 37 pages, 15 figures, accepted for publication in NIM