Inferring Attribute Grammars with Structured Data for Natural Language Processing

Starkie, Bradford

doi:10.1007/3-540-45790-9_19

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…The main challenge for this task is how to obtain automatically suitable semantic derivation rules that can capture the full range of natural language contexts. Despite several approaches proposed to infer grammars and facilitating this task [36][37][38], they all require a large training data set of examples to be effective.…”

Section: Related Workmentioning

confidence: 99%

DeepSTL -- From English Requirements to Signal Temporal Logic

He¹,

Bartocci²,

Ničković³

et al. 2021

Preprint

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Formal methods provide very powerful tools and techniques for the design and analysis of complex systems. Their practical application remains however limited, due to the widely accepted belief that formal methods require extensive expertise and a steep learning curve. Writing correct formal specifications in form of logical formulas is still considered to be a difficult and error prone task.In this paper we propose DeepSTL, a tool and technique for the translation of informal requirements, given as free English sentences, into Signal Temporal Logic (STL), a formal specification language for cyber-physical systems, used both by academia and advanced research labs in industry. A major challenge to devise such a translator is the lack of publicly available informal requirements and formal specifications. We propose a two-step workflow to address this challenge. We first design a grammar-based generation technique of synthetic data, where each output is a random STL formula and its associated set of possible English translations. In the second step, we use a state-of-the-art transformer-based neural translation technique, to train an accurate attentional translator of English to STL. The experimental results show high translation quality for patterns of English requirements that have been well trained, making this workflow promising to be extended for processing more complex translation tasks.

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Section: Related Workmentioning

confidence: 99%

DeepSTL -- From English Requirements to Signal Temporal Logic

He¹,

Bartocci²,

Ničković³

et al. 2021

Preprint

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show abstract

“…With structured data, people can design detailed rules and templates to complete the entire Rule-based natural language generation system [8]. For example, the following is an example generated by a Rule-based natural language generation system implemented by us.…”

Section: B Rule-based Natural Language Generation Systemmentioning

confidence: 99%

A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms

Wei,

Zhou,

Leontidis

2020

Preprint

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This paper proposes an enhanced natural language generation system combining the merits of both rulebased approaches and modern deep learning algorithms, boosting its performance to the extent where the generated textual content is capable of exhibiting agile human-writing styles and the content logic of which is highly controllable. We also come up with a novel approach called HMCU to measure the performance of the natural language processing comprehensively and precisely.

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“…An early release software product for the rapid development of spoken dialog systems (SDS's), known as Lyrebird TM [1][2] [3], will be demonstrated that makes use of grammatical inference to build natural language, mixed initiative, speech recognition applications.…”

Section: Introductionmentioning

confidence: 99%

“…

AbstractAn early release software product for the rapid development of spoken dialog systems (SDS's), known as Lyrebird TM [1][2] [3], will be demonstrated that makes use of grammatical inference to build natural language, mixed initiative, speech recognition applications.The demonstration will consist of the presenter developing a spoken dialog system using Lyrebird TM , and will include a demonstration of some features that are still in the prototype phase.Using Lyrebird TM , developers build a spoken dialog system with the following steps: 1. Firstly the dialog is specified.

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confidence: 99%

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Lyrebird™: Developing Spoken Dialog Systems Using Examples

Starkie¹,

Findlow²,

Ho³

et al. 2002

Grammatical Inference: Algorithms and Applications

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An early release software product for the rapid development of spoken dialog systems (SDS's), known as Lyrebird TM [1][2] [3], will be demonstrated that makes use of grammatical inference to build natural language, mixed initiative, speech recognition applications.The demonstration will consist of the presenter developing a spoken dialog system using Lyrebird TM , and will include a demonstration of some features that are still in the prototype phase.Using Lyrebird TM , developers build a spoken dialog system with the following steps: 1. Firstly the dialog is specified. This is typically developed using a drag and drop GUI that defines a computer directed menu driven application (See Figure. 1). Alternatively a wizard can be used whereby the developer defines the application in terms of slots that need to be filled. 2. The dialog description is then generalised to include mixed initiative input.Starting grammars are automatically created that cover computer directed input. 3. The developer is then prompted for text describing selected phrases that a speaker would use when interacting with the developed SDS (See Figure 2.) Mixed-initiative natural-language grammars suitable for use with a speechrecognition system are then inferred, using grammatical inference.If desired, the developer can subsequently improve the system under development, using a range of interfaces. For instance:1. The developer can teach Lyrebird how the speaker will behave using a tool known as the simulator. The simulator enables the user to interact with the application under development using text. (See Figure 3.) Out of grammar phrases are tagged using either robust parsing, or manual input from the developer. 2. The developer can teach Lyrebird how the system should behave using a tool known as the scenario editor (See Figure 4.) Using the scenario editor sample interactions are presented to the developer in a text format similar to that used

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Inferring Attribute Grammars with Structured Data for Natural Language Processing

Cited by 7 publications

References 4 publications

DeepSTL -- From English Requirements to Signal Temporal Logic

DeepSTL -- From English Requirements to Signal Temporal Logic

A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms

Lyrebird™: Developing Spoken Dialog Systems Using Examples

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