Cross-lingual Sentiment Classification via Multi-view Transfer Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cross-lingual sentiment classification models face challenges in low-resource languages without labeled data or parallel corpora, relying on bilingual word embeddings or parallel resources, which limits their applicability and performance, especially for distant language pairs.
Innovation Solution
A multi-view encoder-classifier (MVEC) system that utilizes an encoder-decoder framework from unsupervised machine translation, integrating a language discriminator to learn refined language-invariant representations, enabling cross-lingual classification without labeled data or parallel resources by generating multiple views for input documents and enforcing consensus between predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-lingual text classification models use bilingual word embeddings or parallel resources, then classification performance is improved, but applicability to low-resource languages without parallel corpora deteriorates
Solution Approach 1:
The patent introduces an unsupervised machine translation system as an intermediary to bridge the source and target languages. The translator generates pseudo-parallel data by translating source language documents into the target language, enabling the model to learn cross-lingual relationships without requiring actual parallel corpora. This mediator allows the system to achieve both high classification performance and broad applicability to low-resource languages.
Solution Approach 2:
The patent performs preliminary unsupervised translation and pseudo-labeling before the actual classification task. By pre-generating translated documents and pseudo-labels for the target language, the system prepares the necessary training data in advance, eliminating the need for actual labeled data or parallel corpora in the target language while maintaining high classification accuracy.
2Measurement precision
If models are trained with labeled data from source language, then classification accuracy is improved, but requirement for labeled data in target language is eliminated
Solution Approach 1:
The patent creates copies of source language documents by translating them into the target language using the unsupervised machine translation system. These translated copies are then assigned pseudo-labels based on the source language labels, creating synthetic labeled data for the target language without requiring actual human annotation. This copying mechanism allows the model to achieve high accuracy while eliminating the need for target language labeled data.
Solution Approach 2:
The system performs self-service by automatically generating its own training data through unsupervised translation and pseudo-labeling. The machine translation system and classification model work together to create labeled training examples for the target language without external intervention, enabling the model to achieve high accuracy while requiring zero labeled data from the target language.
3Adaptability or versatility
If unsupervised machine translation is used to generate pseudo-parallel data, then dependency on parallel resources is eliminated, but model complexity increases
Solution Approach 1:
The patent merges the machine translation system and the classification model into a unified end-to-end training framework. By combining these components and optimizing them jointly with a shared objective function, the system achieves independence from parallel resources while managing complexity through integrated training rather than separate sequential training of individual components.
Solution Approach 2:
The patent creates a universal system that can handle multiple language pairs and classification tasks simultaneously. The unsupervised machine translation component serves multiple functions: generating translated documents, creating pseudo-parallel data, and enabling cross-lingual transfer. This multi-functionality reduces overall system complexity by eliminating the need for separate specialized components for each language pair.
Data Source
AI summary
Presented herein are embodiments of an unsupervised cross-lingual sentiment classification model (which may be referred to as multi-view encoder-classifier (MVEC)) that leverages an unsupervised machine translation (UMT) system and a language discriminator. Unlike previous language model (LM)-based fine-tuning approaches that adjust parameters solely based on the classification error on training data, embodiments employ an encoder-decoder framework of an UMT as a regularization component on the shared network parameters. In one or more embodiments, the cross-lingual encoder of embodiments learns a shared representation, which is effective for both reconstructing input sentences of two languages and generating more representative views from the input for classification. Experiments on five language pairs verify that an MVEC embodiment significantly outperforms other models for 8/11 sentiment classification tasks.