Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction (2501.14144v1)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area with impressive outcomes being achieved on high-resource languages. However, the application of cross-lingual transfer to the ASTE task has been relatively unexplored, and current code-switching methods still suffer from term boundary detection issues and out-of-dictionary problems. In this study, we introduce a novel Test-Time Code-SWitching (TT-CSW) framework, which bridges the gap between the bilingual training phase and the monolingual test-time prediction. During training, a generative model is developed based on bilingual code-switched training data and can produce bilingual ASTE triplets for bilingual inputs. In the testing stage, we employ an alignment-based code-switching technique for test-time augmentation. Extensive experiments on cross-lingual ASTE datasets validate the effectiveness of our proposed method. We achieve an average improvement of 3.7% in terms of weighted-averaged F1 in four datasets with different languages. Additionally, we set a benchmark using ChatGPT and GPT-4, and demonstrate that even smaller generative models fine-tuned with our proposed TT-CSW framework surpass ChatGPT and GPT-4 by 14.2% and 5.0% respectively.

• The paper introduces a novel Test-Time Code-Switching (TT-CSW) framework that leverages alignment-based dynamic code-switching during inference to enhance cross-lingual transfer in ASTE.
• Experimental results show the TT-CSW framework improves weighted-averaged F1 score by 3.7% on average and enables smaller models to outperform ChatGPT and GPT-4 by 14.2% and 5.0% respectively.
• This framework effectively bridges the gap between bilingual training and monolingual testing, demonstrating significant potential for improving cross-lingual NLP tasks, especially in low-resource settings, without requiring massive LLMs.

The paper "Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction" (2501.14144) introduces a novel Test-Time Code-Switching (TT-CSW) framework designed to enhance cross-lingual transfer performance in Aspect Sentiment Triplet Extraction (ASTE). The paper addresses limitations in existing code-switching methods, specifically regarding term boundary detection and out-of-dictionary issues, and bridges the gap between bilingual training and monolingual testing.

TT-CSW Framework

The TT-CSW framework leverages a generative model trained on bilingual code-switched data to produce bilingual ASTE triplets during the training phase. The key innovation lies in the test-time augmentation strategy, which employs an alignment-based code-switching technique. This approach dynamically generates code-switched instances during inference, allowing the model to better generalize to monolingual test data.

Training Phase

The training phase involves creating a generative model using bilingual code-switched data. This model is designed to produce bilingual ASTE triplets, enabling it to understand and generate aspect, sentiment, and opinion target expressions in mixed-language contexts. The paper does not provide specifics on the architecture of the generative model, but one could implement it using sequence-to-sequence models with attention mechanisms, or transformer-based architectures, conditioned on the input sentence and the target language.

Testing Phase

In the testing phase, the framework utilizes an alignment-based code-switching technique for test-time augmentation. Given a monolingual input sentence in the target language, the alignment mechanism identifies potential words or phrases to be code-switched with their translations in the source language. The code-switching is performed dynamically, generating multiple augmented instances of the input sentence. The ASTE model then processes each of these augmented instances, and the final predictions are aggregated or ensembled to produce the final output. The alignment-based code-switching could be implemented using word embeddings and cosine similarity to find the closest corresponding words in the source language.

Experimental Setup

The paper evaluates the TT-CSW framework on cross-lingual ASTE datasets. The experimental setup involves training the model on bilingual data and testing it on monolingual data in different languages. The primary evaluation metric is the weighted-averaged F1 score, which measures the accuracy of the extracted aspect sentiment triplets, considering the precision and recall of each component.

Baselines and Benchmarks

The paper sets benchmarks using LLMs like ChatGPT and GPT-4. This comparison highlights the efficiency and effectiveness of the proposed TT-CSW framework, demonstrating its ability to surpass even the most advanced LLMs with significantly smaller models fine-tuned using the TT-CSW approach.

Performance Analysis

The experimental results demonstrate the effectiveness of the TT-CSW framework, with an average improvement of 3.7% in terms of weighted-averaged F1 score across four datasets with different languages. Furthermore, the paper reports that smaller generative models fine-tuned with the TT-CSW framework outperform ChatGPT and GPT-4 by 14.2% and 5.0%, respectively.

Implications of Performance Gains

These results suggest that the TT-CSW framework is highly effective in bridging the gap between bilingual training and monolingual testing in cross-lingual ASTE. The substantial performance gains compared to state-of-the-art LLMs highlight the potential of the TT-CSW approach for low-resource languages and cross-lingual NLP tasks. The framework's ability to leverage code-switching for test-time augmentation allows it to better generalize to monolingual data, overcoming the limitations of existing methods.

Implementation Considerations

• Computational Requirements: The TT-CSW framework requires computational resources for training the generative model and performing test-time augmentation. The training phase may involve significant GPU memory and processing power, especially when using large-scale transformer models. The test-time augmentation also adds to the computational overhead, as the model needs to process multiple code-switched instances for each input sentence.
• Data Requirements: The framework relies on the availability of bilingual code-switched training data. The quality and diversity of the code-switched data are crucial for the performance of the generative model. Data augmentation techniques can be used to increase the size and diversity of the training data.
• Model Selection: The choice of the generative model and the ASTE model can significantly impact the performance of the TT-CSW framework. Transformer-based models, such as BERT, RoBERTa, and BART, have shown promising results in various NLP tasks and can be fine-tuned for ASTE.
• Alignment Strategy: The alignment-based code-switching technique requires an effective word alignment mechanism. Word embeddings and cosine similarity can be used to find the closest corresponding words in the source language. Attention mechanisms can also be used to capture the contextual relationships between words and phrases.

Conclusion

The TT-CSW framework offers a promising approach for cross-lingual ASTE, effectively leveraging code-switching for test-time augmentation. The experimental results demonstrate significant performance gains compared to existing methods and LLMs. While there are computational and data requirements to consider, the TT-CSW framework has the potential to advance cross-lingual NLP tasks.