- The paper presents RoLASER, a refined LASER model that uses a teacher-student approach to align embeddings of standard and UGC sentences.
- It compares token-level and character-aware architectures, demonstrating up to 11 times better performance on challenging UGC phenomena.
- The approach enhances NLP performance on UGC while also promising improved cross-lingual and cross-modal applications in future research.
Making Sentence Embeddings Robust to User-Generated Content
Introduction
User-generated content (UGC) presents a significant challenge for NLP models, primarily due to its deviation from the "standard" texts these models are commonly trained on. This variance, which includes irregular spellings, evolving slang, and expressions of emotion, often results in a performance drop when these models are applied to UGC. Addressing this, we propose RoLASER, a robust adaptation of the LASER sentence embedding model, specifically designed to mitigate the impact of lexical variations inherent in UGC.
Robustness Challenges in UGC
The performance degradation of NLP models on UGC can be attributed to the semantic vector representations or embeddings not being robust against UGC, where non-standard words and their standard counterparts do not share similar embeddings, although they may have identical meanings within the same context. The challenge extends to common UGC phenomena such as acronyms and misspellings, which significantly affect the tokenization process, hindering the ability of models to represent UGC sentences and their normalized versions in a unified embedding space.
Approach
To combat these challenges, we introduce RoLASER, a refined English encoder trained through a teacher-student approach designed to decrease the distances between the embeddings of standard and UGC sentences. The objective is to align a standard text with its non-standard counterpart in the embedding space, thus improving the robustness of sentence embeddings against UGC. Two model architectures were compared: one token-level and another character-aware, both trained using artificially generated parallel UGC data. The training process emphasizes reducing the standard-UGC distance in the embedding space, aligning with the bitext mining metrics utilized for intrinsic evaluation.
Results
RoLASER has exhibited significant improvements in LASER’s robustness to both natural and artificial UGC, achieving up to 11 times better scores on bitext mining metrics. This enhancement is particularly pronounced in challenging UGC phenomena like keyboard typos and social media abbreviations, where RoLASER vastly outperforms the original LASER model. Furthermore, in downstream tasks, RoLASER demonstrates comparable or superior performance to LASER on standard data while consistently outshining it on UGC data.
Future Directions and Implications
The advancements posed by RoLASER usher in a promising avenue for cross-lingual and cross-modal NLP applications in the field of UGC. Its proficiency in handling lexical variations inherent in UGC not only enhances the performance on such data but also potentially alleviates the data scarcity issue by facilitating the mining of multilingual standard-UGC and UGC-UGC parallel data. Future work could explore extending RoLASER to other languages and their specific UGC phenomena, as well as improving the c-RoLASER model to better map its standard embeddings to LASER's.
In conclusion, RoLASER represents a significant stride toward making sentence embeddings more resilient to the unpredictability of user-generated content, thereby broadening the applicability and effectiveness of NLP models in real-world scenarios dominated by UGC.
