Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

The organization of behavior: A neuropsychological theory

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning , which drastically reduces the number of task-speciﬁc training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model (PaLM). We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly eﬃcient training across multiple TPU Pods. We demonstrate continued beneﬁts of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the ﬁnetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A signiﬁcant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.

PaLM: Scaling Language Modeling with Pathways

Minimal recursion semantics (MRS) is a framework for computational semantics that is suitable for parsing and generation and that can be implemented in typed feature structure formalisms. We discuss why, in general, a semantic representation with minimal structure is desirable and illustrate how a descriptively adequate representation with a nonrecursive structure may be achieved. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. We have integrated MRS with a broad-coverage HPSG grammar.

/pdf/minimal-recursion-semantics-an-introduction-4hdjxn0v8q.pdf

Minimal Recursion Semantics An Introduction

From Discourse to Logic

Proceedings of the 24th International Conference on Computational Linguistics (COLING)

Maurice Gross' grammar lexicon contains rich and exhaustive information about the morphosyntactic and syntactic properties of French syntactic functors (verbs, adjectives, nouns). Yet its use within natural language processing systems is hampered both by its non standard encoding and by a structure that is partly implicit and partly underspecified. In this paper, we present a method for translating this information into a format more amenable for use by NLP systems, we discuss the results obtained so far, we compare our approach with related work and we identify the possible further uses that can be made of the reformatted information.

https://hal.inria.fr/inria-00103163/document

Extraction d’information de sous-catégorisation à partir des tables du LADL

A generation system can only be as good as the data it is trained on. In this short paper , we propose a methodology for analysing data-to-text corpora used for training micro-planner i.e., systems which given some input must produce a text verbalising exactly this input. We apply this methodology to three existing benchmarks and we elicite a set of criteria for the creation of a data-to-text benchmark which could help better support the development , evaluation and comparison of linguistically sophisticated data-to-text generators.

/pdf/analysing-data-to-text-generation-benchmarks-352k9gs0u6.pdf

Analysing Data-To-Text Generation Benchmarks

While much work on automated ontology enrichment has focused on mining text for concepts and relations, little attention has been paid to the task of enriching ontologies with complex axioms In this paper, we focus on a form of text that is frequent in industry, namely system installation design principle (SIDP) and we present a framework which can be used both to map SIDPs to OWL DL axioms and to assess the quality of these automatically derived axioms We present experimental results on a set of 960 SIDPs provided by Airbus which demonstrate (i) that the approach is robust (9750% of the SIDPs can be parsed) and (ii) that DL axioms assigned to full parses are very likely to be correct in 96% of the cases

/pdf/mapping-natural-language-to-description-logic-3fqzr5mk9v.pdf

Mapping Natural Language to Description Logic

WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing). Compared to the eponymous WebNLG challenge, WebNLG+ provides an extended dataset that enable the training, evaluation, and comparison of microplanners and semantic parsers. In this paper, we present the results of the generation and semantic parsing task for both English and Russian and provide a brief description of the participating systems.

https://hal.archives-ouvertes.fr/hal-03148418/document

Claire Gardent

Papers

Proceedings of the 24th International Conference on Computational Linguistics (COLING)

Extraction d’information de sous-catégorisation à partir des tables du LADL

Analysing Data-To-Text Generation Benchmarks

Mapping Natural Language to Description Logic

The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)