Persuasive rather than ‘binding’ EU soft law? An argumentative perspective on the European Commission’s soft law instruments in times of crisis

This paper starts from the premise that argumentation in EU (Commission) soft law instruments is essential for their effectiveness, mainly due to its function to persuade addressees as a means to enhance compliance. Notwithstanding their importance in the EU legal-political landscape, the problem is how to ensure that these instruments devoid of formal legally binding force can function as effective governance tools by convincing addressees to comply, particularly during crisis periods such as the Covid-19 crisis, when fast and effective action is urgently needed. By pointing at a number of significant legal problems and concerns deriving from the Commission’s ‘hardened’ soft law instruments, we suggest a normative approach focusing on the potential of EU soft law instruments to act as highly persuasive tools. By making the instruments’ argumentation a core concern, we examine its role as a means to improve the intrinsic quality of EU (Commission) soft law and to foster effective compliance. To this end, we propose a theoretical-analytical framework combining insights from law and argumentation theory, that puts forward an argumentative toolbox for the analysis and assessment of EU (Commission) soft law instruments. This toolbox comprises four argumentative parameters that need to be taken into account in the drafting and evaluation of EU (Commission) soft law instruments: (1) the content of the argumentation, (2) the design of the arguments pointing at persuasive suggestions for cooperation, (3) the factors influencing argumentative effectiveness, and (4) the soundness of argumentation

Argumentative patterns in the European Union directives:An effective tool to foster compliance by the Member States

Abstract This paper provides an account of the arguments advanced by the European Union (EU) legislator in the preamble of directives adopted for harmonization in the internal market, and assesses them as to their potential at convincing the Member States to implement the directive at issue. We show what directives should argue for and how they do so in practice, by focussing in particular on Directive 2011/83/EU on consumer rights. Furthermore, this contribution moves beyond a purely academic discussion by linking the theoretical-normative framework advanced to the Court of Justice of the European Union’s approach to assessing the preambles of EU directives in the context of the ‘check’ on the duty to state reasons under Article 296 Treaty for the of the Functioning of the European Union (TFEU). Our analysis unveils a legislative practice in which the obligation to give reasons is not discharged adequately from an argumentative perspective, and which remains generally unsanctioned due to the rather light and flexible test used by CJEU under Article 296 TFEU.</jats:p

Strong and Efficient Baselines for Open Domain Conversational Question Answering

Unlike the Open Domain Question Answering (ODQA) setting, the conversational (ODConvQA) domain has received limited attention when it comes to reevaluating baselines for both efficiency and effectiveness. In this paper, we study the State-of-the-Art (SotA) Dense Passage Retrieval (DPR) retriever and Fusion-in-Decoder (FiD) reader pipeline, and show that it significantly underperforms when applied to ODConvQA tasks due to various limitations. We then propose and evaluate strong yet simple and efficient baselines, by introducing a fast reranking component between the retriever and the reader, and by performing targeted finetuning steps. Experiments on two ODConvQA tasks, namely TopiOCQA and OR-QuAC, show that our method improves the SotA results, while reducing reader's latency by 60%. Finally, we provide new and valuable insights into the development of challenging baselines that serve as a reference for future, more intricate approaches, including those that leverage Large Language Models (LLMs).Comment: Accepted to EMNLP 2023 Finding

INFECTION WITH PASTEURELLA SPP. IN A NEW ZEALAND RABBIT COLONY

ABSTRACT The purpose of the research was to establish the etiology and to fight against the acute respiratory syndrome to New Zealand rabbits, of all ages to which the morbidity and mortality were high, from a farm with intensive breeding. Clinical 760 rabbits were examined, adults, young and new born raised in wire net cages in a closed stable, without draught. The necropsy examination was performed in laboratory on 57 dead rabbits with acute respiratory and also cultivations from trachea, lung, liver and spinal marrow were carried out. We used the bacterioscopic technique, cultivation on usual and selective media for Pasteurella, Mycoplasma and Streptococcus for isolation and identification of the micro-organisms present in the respiratory tract. Out of the 57 examined samples, 28 ones were positive and identified as Pasteurella spp., of which 21 were isolated from trachea, lung and liver and 7 from spinal marrow. Seventeen samples of the 28 examined ones contained both Pasteurella spp. and Mycoplasma spp. and 11 samples Pasteurella spp. and Streptococcus spp. also isolated and identified in trachea and lung. The antibiogram test used bio-discs with penicillin, amoxicillin, gentamicin, spectinomicin, oxytetracycline, erythromycin, enrofloxacin and cefaclor. The rabbits were divided into two groups: the first group was treated with enrofloxacin for 5 days, and the second group was treated with oxytetracycline for 5 days, both administered in drinking water. One day after the beginning of the treatment the rabbits got better and the mortality stopped after the fourth day of treatment

Transformers as Graph-to-Graph Models

We argue that Transformers are essentially graph-to-graph models, with sequences just being a special case. Attention weights are functionally equivalent to graph edges. Our Graph-to-Graph Transformer architecture makes this ability explicit, by inputting graph edges into the attention weight computations and predicting graph edges with attention-like functions, thereby integrating explicit graphs into the latent graphs learned by pretrained Transformers. Adding iterative graph refinement provides a joint embedding of input, output, and latent graphs, allowing non-autoregressive graph prediction to optimise the complete graph without any bespoke pipeline or decoding strategy. Empirical results show that this architecture achieves state-of-the-art accuracies for modelling a variety of linguistic structures, integrating very effectively with the latent linguistic representations learned by pretraining.Comment: Accepted to Big Picture workshop at EMNLP 202

The geometric algebra of Fierz identities in arbitrary dimensions and signatures

We use geometric algebra techniques to give a synthetic and computationally efficient approach to Fierz identities in arbitrary dimensions and signatures, thus generalizing previous work. Our approach leads to a formulation which displays the underlying real, complex or quaternionic structure in an explicit and conceptually clear manner and is amenable to implementation in various symbolic computation systems. We illustrate our methods and results with a few examples which display the basic features of the three classes of pin representations governing the structure of such identities in various dimensions and signatures.Comment: 77 pages; version published in JHEP in 201