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

This paper defines a language L for specifying LFG grammars. This enables constraints on LFG's composite ontology (c-structures synchronised with f-structures) to be stated directly; no appeal to the LFG construction algorithm is needed. We use L to specify schemata annotated rules and the LFG uniqueness, completeness and coherence principles. Broader issues raised by this work are noted and discussed.

A specification language for Lexical Functional Grammars

We present a normalisation framework for linguistic representations and illustrate its use by normalising the Stanford Dependency graphs (SDs) produced by the Stanford parser into Labelled Stanford Dependency graphs (LSDs). The normalised representations are evaluated both on a testsuite of constructed examples and on free text. The resulting representations improve on standard Predicate/Argument structures produced by SRL by combining role labelling with the semantically oriented features of SDs. Furthermore, the proposed normalisation framework opens the way to stronger normalisation processes which should be useful in reducing the burden on inference.

https://www.aclweb.org/anthology/W09-3744.pdf

Semantic Normalisation : a Framework and an Experiment

Recently, several data-sets associating data to text have been created to train data-to-text surface realisers. It is unclear however to what extent the surface realisation task exercised by these data-sets is linguistically challenging. Do these data-sets provide enough variety to encourage the development of generic, high-quality data-to-text surface realisers ? In this paper, we argue that these data-sets have important drawbacks. We back up our claim using statistics, metrics and manual evaluation. We conclude by eliciting 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 surface realisers.

Analysing Data-To-Text Generation Benchmarks

This paper describes an interactive learning system specifically designed for second language acquisition. In order to render the learning experience more fun, to engage the learner and to help him maintaining long-term motivation, the system was implemented as a 3D video game. It brings together the ability of virtual reality environments such as Second Life to reproduce immersive experiences and NLP language technology, thereby provided both situated learning and and automatic authoring of training activities in context

A Serious Game for Second Language Acquisition

Finding the natural language equivalent of structured data is both a challenging and promising task. In particular, an efficient alignment of knowledge bases with texts would benefit many applications, including natural language generation, information retrieval and text simplification. In this paper, we present an approach to build a dataset of triples aligned with equivalent sentences written in natural language. Our approach consists of three main steps. First, target sentences are annotated automatically with knowledge base (KB) concepts and instances. The triples linking these elements in the KB are extracted as candidate facts to be aligned with the annotated sentence. Second, we use textual mentions referring to the subject and object of these facts to semantically simplify the target sentence via crowdsourcing. Third, the sentences provided by different contributors are post-processed to keep only the most relevant simplifications for the alignment with KB facts. We present different filtering methods, and share the constructed datasets in the public domain. These datasets contain 1050 sentences aligned with 1885 triples. They can be used to train natural language generators as well as semantic or contextual text simplifiers.

/pdf/aligning-texts-and-knowledge-bases-with-semantic-sentence-458666brpo.pdf

Claire Gardent

Papers

A specification language for Lexical Functional Grammars

Semantic Normalisation : a Framework and an Experiment

Analysing Data-To-Text Generation Benchmarks

A Serious Game for Second Language Acquisition

Aligning Texts and Knowledge Bases with Semantic Sentence Simplification