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

We present two dialogue systems developed to support chating with French speaking virtual characters in the context of a serious game: one hybrid statistical/symbolic and one purely statistical. The player is guided in the quest by different interactions including twelve distinct dialogs with different virtual characters.

https://hal.inria.fr/hal-00757892/document

What should I do now? Supporting conversations in a serious game.

Cet article decrit une methodologie visant la realisation d’une ressource semantique en francais centree sur la synonymie. De maniere complementaire aux travaux existants, la methode proposee n’a pas seulement pour objectif d’etablir des liens de synonymie entre lexemes, mais egalement d’apparier les sens possibles d’un lexeme avec les ensembles de synonymes appropries. En pratique, les sens possibles des lexemes proviennent des definitions du TLFi et les synonymes de cinq dictionnaires accessibles a l’ATILF. Pour evaluer la methode d’appariement entre sens d’un lexeme et ensemble de synonymes, une ressource de reference a ete realisee pour 27 verbes du francais par quatre lexicographes qui ont specifie manuellement l’association entre verbe, sens (definition TLFi) et ensemble de synonymes. Relativement a ce standard etalon, la methode d’appariement affiche une F-mesure de 0.706 lorsque l’ensemble des parametres est pris en compte, notamment la distinction pronominal / non-pronominal pour les verbes du francais et de 0.602 sans cette distinction.

/pdf/sens-synonymes-et-definitions-438l8arb90.pdf

Sens, synonymes et définitions

Although much has been said about parallelism in discourse, a formal, computational theory of parallelism structure is still outstanding. In this paper, we present a theory which given two parallel utterances predicts which are the parallel elements. The theory consists of a sorted, higher-order abductive calculus and we show that it reconciles the insights of discourse theories of parallelism with those of Higher-Order Unification approaches to discourse semantics, thereby providing a natural framework in which to capture the effect of parallelism on discourse semantics.

Computing Parallelism in Discourse

The Natural Language Pipeline, Neural Text Generation and Explainability.

We present a method for grouping the synonyms of a lemma according to its dictionary senses. The senses are defined by a large machine read- able dictionary for French, the TLFi (Tresor de la langue francaise informatise) and the syn- onyms are given by 5 synonym dictionaries (also for French). To evaluate the proposed method, we manually constructed a gold standard where for each (word, definition) pair and given the set of synonyms defined for that word by the 5 synonym dictionaries, 4 lexicographers speci- fied the set of synonyms they judge adequate. While inter-annotator agreement ranges on that task from 67% to at best 88% depending on the annotator pair and on the synonym dictionary being considered, the automatic procedure we propose scores a precision of 67% and a recall of 71%. The proposed method is compared with related work namely, word sense disambiguation, synonym lexicon acquisition and WordNet con- struction.

https://hal.inria.fr/inria-00418458/document

Claire Gardent

Papers

What should I do now? Supporting conversations in a serious game.

Sens, synonymes et définitions

Computing Parallelism in Discourse

The Natural Language Pipeline, Neural Text Generation and Explainability.

Grouping Synonyms by Definitions