Temporal Attention for Language Models
This addresses the issue of temporal generalization in NLP for researchers and practitioners, though it is incremental as it builds on existing transformer architectures.
The authors tackled the problem of language models ignoring temporal information in web-sourced text by proposing temporal attention, a time-aware self-attention mechanism for transformers, which achieved state-of-the-art results on semantic change detection across three multilingual datasets.
Pretrained language models based on the transformer architecture have shown great success in NLP. Textual training data often comes from the web and is thus tagged with time-specific information, but most language models ignore this information. They are trained on the textual data alone, limiting their ability to generalize temporally. In this work, we extend the key component of the transformer architecture, i.e., the self-attention mechanism, and propose temporal attention - a time-aware self-attention mechanism. Temporal attention can be applied to any transformer model and requires the input texts to be accompanied with their relevant time points. It allows the transformer to capture this temporal information and create time-specific contextualized word representations. We leverage these representations for the task of semantic change detection; we apply our proposed mechanism to BERT and experiment on three datasets in different languages (English, German, and Latin) that also vary in time, size, and genre. Our proposed model achieves state-of-the-art results on all the datasets.