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

Parsing with categorial grammars often leads to problems such as proliferating lexical ambiguity, spurious parses and overgeneration. This paper presents a parser for French developed on an unification based categorial grammar (FG) which avoids these problems. This parser is a bottom-up chart parser augmented with a heuristic eliminating spurious parses. The unicity and completeness of parsing are proved.

https://halshs.archives-ouvertes.fr/halshs-00372914/document

Efficient parsing for french

We claim that existing specification languages for tree based grammars fail to adequately support identifier managment. We then show that XMG (eXtensible Meta-Grammar) provides a sophisticated treatment of identifiers which is effective in supporting a linguist-friendly grammar design.

Coreference Handling in XMG

While in Computer Science, grammar engineering has led to the development of various tools for checking grammar coherence, completion, under- and over-generation, in Natural Langage Processing, most approaches developed to improve a grammar have focused on detecting under-generation and to a much lesser extent, over-generation. We argue that generation can be exploited to address other issues that are relevant to grammar engineering such as in particular, detecting grammar incompleteness, identifying sources of over-generation and analysing the linguistic coverage of the grammar. We present an algorithm that implements these functionalities and we report on experiments using this algorithm to analyse a Feature-Based Lexicalised Tree Adjoining Grammar consisting of roughly 1500 elementary trees.

https://hal.archives-ouvertes.fr/hal-00768612/document

Generation for Grammar Engineering

Seq2seq models based on Recurrent Neural Networks (RNNs) have recently received a lot of attention in the domain of Semantic Parsing for Question Answering. While in principle they can be trained directly on pairs (natural language utterances, logical forms), their performance is limited by the amount of available data. To alleviate this problem, we propose to exploit various sources of prior knowledge: the well-formedness of the logical forms is modeled by a weighted context-free grammar; the likelihood that certain entities present in the input utterance are also present in the logical form is modeled by weighted finite-state automata. The grammar and automata are combined together through an efficient intersection algorithm to form a soft guide ("background") to the RNN. We test our method on an extension of the Overnight dataset and show that it not only strongly improves over an RNN baseline, but also outperforms non-RNN models based on rich sets of hand-crafted features.

Symbolic Priors for RNN-based Semantic Parsing

I show that the equational treatment of ellipsis proposed in (Dalrymple et al., 1991) can further be viewed as modeling the effect of parallelism on semantic interpretation. I illustrate this claim by showing that the account straightforwardly extends to a general treatment of sloppy identity on the one hand, and to deaccented foci on the other. I also briefly discuss the results obtained in a prototype implementation.

Claire Gardent

Papers

Efficient parsing for french

Coreference Handling in XMG

Generation for Grammar Engineering

Symbolic Priors for RNN-based Semantic Parsing

Unifying Parallels