This study is the first likelihood ratio (LR)based forensic text comparison study in which each text is mapped onto an embedding vector using RoBERTa as the pre-trained model. The scores obtained with Cosine distance and probabilistic linear discriminant analysis (PLDA) were calibrated to LRs with logistic regression; the quality of the LRs was assessed by log LR cost (𝐶𝑙𝑙𝑟). Although the documents in the experiments were very short (maximum 100 words), the systems reached the 𝐶𝑙𝑙𝑟values of 0.55595 and 0.71591 for the Cosine and PLDA systems, respectively. The effectiveness of deep-learning-based text representation is discussed by comparing the results of the current study to those of the previous studies of systems based on conventional feature engineering tested with longer documents.
|Publication status||Published - 2022|
|Event||The 20th Annual Workshop of the Australasian Language Technology Association - Adelaide, SA|
Duration: 1 Jan 2022 → …
|Conference||The 20th Annual Workshop of the Australasian Language Technology Association|
|Period||1/01/22 → …|