แนวคิดหลัก
Linguistic variation poses significant challenges for language models, requiring careful consideration of data characteristics and model capabilities to facilitate effective adaptation.
บทคัดย่อ
The paper presents a suite of 10 interventions that synthetically induce different types of linguistic variation, including orthographic, subword boundary, morphosyntactic, and lexicosemantic changes. The authors conduct a series of experiments to evaluate how well BERT and multilingual BERT (mBERT) models can adapt to these variations under different fine-tuning data conditions.
Key insights:
- Out-of-the-box, the language models demonstrate extremely low performance on all types of linguistic variation, highlighting the need for new adaptation methods.
- The composition of fine-tuning data is crucial - models perform better when the data is fully modified by the interventions rather than a mix of standard and nonstandard text.
- The amount of fine-tuning data needed varies by the type of linguistic variation:
- Orthographic and morphosyntactic variations can be learned with relatively small amounts of data.
- Lexicosemantic variations require much larger amounts of data to see a breakthrough in performance.
- Monolingual BERT outperforms multilingual BERT on orthographic and morphosyntactic variations, while mBERT has an advantage for lexicosemantic variations, likely due to its broader linguistic knowledge.
- The authors provide guidelines and a publicly available suite of interventions to facilitate future research on making language models more robust to linguistic variation.
สถิติ
"Out-of-the-box performance (data amount 0) is best when there is no intervention, but very low across the interventions."
"Access to more data is vital when dealing with lexical and semantic variation (e.g., Spanish varieties, Italian dialects). Multilingual models are also more helpful in such cases."
"In contrast, the amount of data is not as important for varieties exhibiting more spelling variation (e.g., Germanic languages and varieties), and robustness to such variation will likely require another solution besides more data."
คำพูด
"Linguistic variation is all around us. Whether a user adopts a regional dialect, follows different spelling conventions, or uses culturally-specific vocabulary, encountering linguistic variation in most day-to-day NLP use cases is inevitable."
"As larger and larger language models with newfound capabilities continue to emerge, the NLP community also continues to find that dealing with linguistic variation (e.g., dialects, language varieties, and noisy text) remains a challenge."
"To this end, we develop a set of experiments that isolate data-related factors that can play a role in language model adaptation (e.g., type, amount, and composition of training data), and we assemble a suite of ten interventions to synthetically induce different forms of linguistic variation (e.g., orthographic, morphosyntactic, lexicosemantic) in controlled settings."