Multilingual Sentiment Annotation With Translation Alignment Filtering
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Solution Overview
Problem
Existing multilingual aspect-based sentiment analysis (ABSA) systems face challenges in generating high-quality training datasets across languages due to the scarcity of publicly available resources, costly manual annotation, and inaccuracies from machine translation, leading to inconsistent sentiment analysis results.
Innovation Solution
A system leveraging a generative instruction-tuned language model to create synthetic English sentences with labeled sentiments, translating them into other languages, and using token-level alignment and confidence-based scoring to validate sentiment consistency, followed by a data filtration process to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine translation is used to expand ABSA capabilities to additional languages, then the system can process multiple languages, but translation errors are introduced in aspect localization and sentiment consistency
Solution Approach 1:
The system implements a feedback mechanism where the translated aspect-sentiment pairs are fed back into the alignment model for validation. The alignment model compares the translated aspects with the original English aspects and adjusts the translation quality assessment accordingly, allowing iterative improvement of translation accuracy for multilingual ABSA
Solution Approach 2:
The patent introduces an alignment model as an intermediary component between the translation system and the ABSA model. This intermediary validates the translation quality by checking aspect localization accuracy and sentiment consistency, acting as a mediator that filters out erroneous translations before they enter the training pipeline
2Measurement precision
If manual annotation is used to create high-quality ABSA training datasets, then annotation quality is high, but the process is costly and labor-intensive
Solution Approach 1:
The system creates synthetic training data by copying and transforming existing high-quality English aspect-sentiment pairs into multiple languages through translation. This copying approach allows rapid generation of multilingual training data while preserving the quality characteristics of the original annotated data, eliminating the need for manual annotation in each target language
Solution Approach 2:
The patent performs preliminary action by first creating and validating high-quality English aspect-sentiment pairs before translating them to other languages. This preliminary preparation ensures that the source data is optimized for translation quality, allowing efficient generation of multilingual training datasets without requiring manual annotation in each target language
3Reliability
If automated filtering techniques are implemented to validate translation quality, then translation errors can be reduced, but the system complexity increases
Solution Approach 1:
The alignment model serves multiple functions simultaneously: it validates aspect localization accuracy, checks sentiment consistency, and assesses translation quality. This multi-functionality reduces the need for separate validation components, thereby limiting the increase in system complexity while maintaining high data quality through comprehensive filtering
Data Source
AI summary
A method for generating multilingual aspect-based sentiment annotations in different languages includes, by a computing system, receiving first content in a first language and performing an inference of the first content for presence of a plurality of aspects, including identifying aspects within the first content, annotating the first content in accordance with the identified aspects within the first content, and generating an annotated first content. The method further includes receiving second content in a second language, including a translation of the first content, performing the inference of the second content for presence of the aspects to generate an annotated second content and producing a training set in the second language from the annotated second content. The training set is suitable for use, in the second language, in refining the inference in classifying portions of the second content into one of a plurality of polarities associated with the plurality of aspects.