Meaning versus Grammar

This volume investigates the complicated relationship between grammar, computation, and meaning in natural languages. It details conditions under which meaning-driven processing of natural language is feasible, discusses an operational and accessible implementation of the grammatical cycle for Dutch, and offers analyses of a number of further conjectures about constituency and entailment in natural language

Natural Language Syntax Complies with the Free-Energy Principle

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design - such as "minimal search" criteria from theoretical syntax - adhere to the FEP. This affords a greater degree of explanatory power to the FEP - with respect to higher language functions - and offers linguistics a grounding in first principles with respect to computability. We show how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference

Evaluating visually grounded language capabilities using microworlds

Deep learning has had a transformative impact on computer vision and natural language processing. As a result, recent years have seen the introduction of more ambitious holistic understanding tasks, comprising a broad set of reasoning abilities. Datasets in this context typically act not just as application-focused benchmark, but also as basis to examine higher-level model capabilities. This thesis argues that emerging issues related to dataset quality, experimental practice and learned model behaviour are symptoms of the inappropriate use of benchmark datasets for capability-focused assessment. To address this deficiency, a new evaluation methodology is proposed here, which specifically targets in-depth investigation of model performance based on configurable data simulators. This focus on analysing system behaviour is complementary to the use of monolithic datasets as application-focused comparative benchmarks. Visual question answering is an example of a modern holistic understanding task, unifying a range of abilities around visually grounded language understanding in a single problem statement. It has also been an early example for which some of the aforementioned issues were identified. To illustrate the new evaluation approach, this thesis introduces ShapeWorld, a diagnostic data generation framework. Its design is guided by the goal to provide a configurable and extensible testbed for the domain of visually grounded language understanding. Based on ShapeWorld data, the strengths and weaknesses of various state-of-the-art visual question answering models are analysed and compared in detail, with respect to their ability to correctly handle statements involving, for instance, spatial relations or numbers. Finally, three case studies illustrate the versatility of this approach and the ShapeWorld generation framework: an investigation of multi-task and curriculum learning, a replication of a psycholinguistic study for deep learning models, and an exploration of a new approach to assess generative tasks like image captioning.Qualcomm Award Premium Research Studentship, Engineering and Physical Sciences Research Council Doctoral Training Studentshi

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)