Code-Mixed Word Translation via Contextual Portion Segmentation
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Solution Overview
Problem
Existing language translation applications are unable to effectively translate code-mixed words, which are combinations of words from different languages, as they are designed to translate only typical words of one language to another and lack effectiveness in handling code-mixing at the word level.
Innovation Solution
A method and device that identify and replace portions of code-mixed words with equivalent words in a predetermined target language based on context within a sentence, using a processor and memory to determine and select appropriate equivalents, allowing for the translation of code-mixed words into uni-language words or vice versa.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing language translation applications are used, then typical words of one language can be translated to another language, but code-mixed words cannot be translated
Solution Approach 1:
The code-mixed word is segmented into multiple portions, where each portion belongs to a different language. The system identifies language boundaries within the word and processes each portion separately through language-specific translation models, then combines them to form the final translation.
Solution Approach 2:
The system introduces an intermediary language identification step that detects which portions of the code-mixed word belong to which language. This intermediary process enables the translation system to route different portions through appropriate translation pathways before combining the results.
2Adaptability or versatility
If code-mixed words are translated by translating each portion separately, then translation coverage is improved, but context accuracy may be compromised
Solution Approach 1:
The system uses contextual feedback from the surrounding sentence to guide the selection and combination of translated portions. The context information is fed back into the translation process to ensure that the combined translation of code-mixed portions maintains grammatical correctness and semantic coherence with the overall sentence meaning.
Solution Approach 2:
The system dynamically adjusts translation parameters based on the detected language portions and their positions within the code-mixed word. Different translation strategies and models are selected for different language portions based on their linguistic characteristics and contextual requirements.
3Productivity
If Natural Language Processing techniques are used for sentence-level code-mixing, then sentence translation is improved, but word-level code-mixing translation remains ineffective
Solution Approach 1:
The system segments the code-mixed sentence into individual words and further segments code-mixed words into language-specific portions. This fine-grained segmentation enables word-level translation processing while maintaining the efficiency benefits of automated NLP techniques at the sentence level.
Solution Approach 2:
The system adds a new dimension of analysis by examining language composition within individual words, rather than only at the sentence or word level. This dimensional change from sentence-level to portion-level analysis enables effective translation of word-level code-mixing while preserving NLP efficiency.
Data Source
AI summary
A method and a systems of interchanging code-mixed words and uni-language words are described. In an embodiment, the method may include identifying two or more portions of a target word, wherein the target word is one of a code-mixed word and a uni-language word. The method may further include determining one or more equivalent words corresponding to at least one portion of the two or more portions, wherein the one or more equivalent words are in at least one predetermined target language. The method may further include selecting at least one equivalent word from the one or more equivalent words, based on a context of the target word within a sentence comprising the target word. The method may further include replacing the at least one portion of the two or more portions of the target word with the selected at least one equivalent word.