Semantic similarity is the measure of the similarity between the meaning of two text sequences. Recurrent Neural Networks (RNNs) can be used to compute semantic similarity by first encoding the text sequences into fixed-length vector representations, and then comparing the vectors using a similarity metric, such as cosine similarity. This can be achieved using a variety of techniques such as bi-directional RNNs or attention-based mechanisms, that allow the model to consider the context of each word in the sequence while computing the vector representation. The trained model can then be used to determine the similarity between pairs of text sequences. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can be fine-tuned or used as a feature extractor to compute semantic similarity. BERT represents each word in a text sequence as a vector, and uses attention mechanisms to consider the context of each word in the sequence when computing its representation.
The training process typically involves feeding the model large amounts of text data from the SLNI corpus and adjusting the model's parameters to minimize the error between the model's predictions and the true next words in the text. This process is iteratively repeated until the model reaches a satisfactory level of accuracy. Once the model has been trained, it can be fine-tuned on specific tasks such as natural language understanding, language translation, or text generation by using smaller, task-specific datasets.
The SLNI corpus and SICK is considered as important datasets for training models and is being used by many research and industry groups in order to improve the performance of their models.