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Generating Natural Language Under Pragmatic Constraints

Brayden Reed

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Published in Eduard H Hovy

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Natural language generation (NLG) is the task of producing text or speech from structured data. While NLG systems have made significant progress in recent years, they still face challenges in generating natural language that is both coherent and appropriate for a given context.

Pragmatic constraints are a set of rules that govern how language is used in different situations. These constraints include factors such as the speaker's intention, the audience's knowledge and beliefs, and the social context.

Generating natural language under pragmatic constraints is a challenging task. However, it is essential for NLG systems to be able to produce text that is not only coherent but also appropriate for a given context.

Generating Natural Language Under Pragmatic Constraints

by Eduard H. Hovy

5 out of 5

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There are a number of different approaches to generating natural language under pragmatic constraints. One common approach is to use a rule-based system. Rule-based systems use a set of predefined rules to generate text. These rules can be based on linguistic principles, pragmatic principles, or a combination of both.

Another approach to generating natural language under pragmatic constraints is to use a data-driven system. Data-driven systems learn from a corpus of natural language text. They can then use this knowledge to generate new text that is both coherent and appropriate for a given context.

Finally, some NLG systems use a hybrid approach that combines rule-based and data-driven techniques. Hybrid systems can benefit from the strengths of both approaches.

There are a number of challenges in generating natural language under pragmatic constraints. One challenge is the difficulty of representing pragmatic constraints in a formal way. Pragmatic constraints are often implicit and context-dependent. This makes it difficult to develop rules or algorithms that can capture all of the relevant factors.

Another challenge is the need to balance coherence and appropriateness. NLG systems must generate text that is both coherent and appropriate for a given context. This can be difficult to achieve, as the two goals can sometimes be in conflict.

Finally, NLG systems must be able to handle a wide range of input data. NLG systems may be required to generate text from structured data, unstructured data, or a combination of both. This can be a challenging task, as the input data may be incomplete, ambiguous, or contradictory.

Generating natural language under pragmatic constraints has a wide range of applications. These applications include:

Text summarization: NLG systems can be used to summarize text documents. This can be useful for a variety of purposes, such as news summarization, scientific abstract generation, and legal document summarization.
Machine translation: NLG systems can be used to translate text from one language to another. This can be useful for a variety of purposes, such as business communication, travel, and education.
Dialogue systems: NLG systems can be used to generate text for dialogue systems. This can be useful for a variety of purposes, such as customer service, technical support, and information retrieval.
Report generation: NLG systems can be used to generate reports from structured data. This can be useful for a variety of purposes, such as financial reporting, medical reporting, and scientific reporting.

Generating natural language under pragmatic constraints is a challenging but important task. NLG systems that can generate coherent and appropriate text have a wide range of applications. As NLG technology continues to improve, we can expect to see even more innovative and useful applications for this technology in the future.

Generating Natural Language Under Pragmatic Constraints

by Eduard H. Hovy

5 out of 5