Weather Report Translation Into LIS Using Rule-Based Gloss Sorting
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Solution Overview
Problem
Existing automatic translation systems for sign languages face challenges due to small corpora size, high costs, and algorithmic complexity, leading to increased translation errors and the need for costly apparatuses, especially when translating weather reports into Italian Sign Language (LIS).
Innovation Solution
A deterministic algorithm that analyzes the input text, substitutes and partitions it into sentences, sorts the sentences according to LIS syntax, and eliminates stop-words to generate a sequence of LIS signs, using a finite set of weather report-specific glosses and rules to reduce errors and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning systems are used for automatic translation, then translation capability is improved, but apparatus cost and algorithmic complexity increase
Solution Approach 1:
The translation system is segmented into three independent modules: a lexical module for word-level translation, a syntactic module for sentence structure analysis, and a semantic module for meaning preservation. This segmentation allows each module to perform a specific function with simpler algorithms, reducing overall system complexity while maintaining translation capability.
Solution Approach 2:
The syntactic module implements a universal grammar framework that handles multiple sentence structures and languages through a single set of rules. This multi-functional approach eliminates the need for separate complex algorithms for different translation scenarios, reducing apparatus cost and algorithmic complexity.
2Measurement precision
If large corpora are used for training, then translation accuracy is improved, but resource requirements and costs increase
Solution Approach 1:
The system uses weather report-specific corpora that are automatically generated and refined through the translation process itself. The syntactic and semantic modules enable the system to learn from structured weather report patterns without requiring vast external corpora, making the system self-sufficient with minimal training resources.
Solution Approach 2:
The system changes the parameter of corpora size from large general-purpose collections to small domain-specific collections. By focusing on weather report terminology and structures, the system achieves high translation accuracy with minimal corpora, as the structured nature of weather reports provides sufficient training data for the modular algorithms.
3Ease of manufacture
If small corpora are used, then resource requirements are reduced, but translation errors increase
Solution Approach 1:
The syntactic module performs preliminary action by analyzing and structuring sentences before translation. This preliminary syntactic analysis ensures that even with small corpora, the translation process maintains grammatical correctness and structural accuracy, reducing translation errors while keeping resource requirements low.
Solution Approach 2:
The semantic module acts as an intermediary between the lexical translation and the final output. It ensures that the meaning is preserved accurately by mediating the translation process, which compensates for the limitations of small corpora and reduces translation errors without requiring additional resources.
4Reliability
If complex algorithms are used, then translation capability is improved, but operating and maintenance costs increase
Solution Approach 1:
By segmenting the translation system into three simple modular components, each performing a specific function, the computational complexity of each module is reduced. This segmentation allows the system to maintain translation capability while using less computational energy, thereby reducing operating and maintenance costs.
Solution Approach 2:
The system replaces complex machine learning algorithms with a rule-based mechanical system consisting of lexical, syntactic, and semantic modules. This substitution eliminates the need for computationally intensive training and inference processes, significantly reducing energy consumption and operating costs while maintaining translation capability.
Data Source
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AI summary
The present invention relates to a method for automatic translation of a text of a weather report into a text representative of a sequence of signs of a sign language, by means of a translating device comprising processing means (140), said method comprising: a substitution phase, wherein the expressions contained in the text of the weather report are substituted, by said processing means (140), with a single gloss or a sequence of glosses, which can be associated with the signs of said sign language; a partitioning phase, wherein the text obtained from the operations carried out in said substitution phase is partitioned, by said processing means (140), into one or more cells adapted to determine the basic linguistic structure necessary and sufficient for defining the condition of a meteorological situation; an elimination phase, wherein said processing means (140) eliminate the stop -words from each cell of said sequence of cells obtained in said partitioning phase; a sorting phase, wherein the terms of each cell of said sequence of cells obtained in said elimination phase are sorted, by said processing means (140), according to a predetermined order.