Method, apparatus, electronic device and storage medium for training semantic similarity model

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected with the at least one processor;
• wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a method for training a semantic similarity model for a parameter fine-tuning of a semantic matching task of Natural Language Processing (NLP), the semantic similarity model being configured to receive an input of two texts, calculate the similarity between the two texts, and output a semantic similarity, wherein the method comprises:
• obtaining a target field to be used by a semantic similarity model to be trained;
• calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets;
• training the semantic similarity model with the training datasets in turn, according to an ascending order of the respective correlations between the target field and the application fields corresponding to each of the training datasets,
• wherein the training the semantic similarity model with the training datasets in turn, according to an ascending order of the respective correlations between the target field and the application fields corresponding to each of the training datasets comprises:
• according to the respective correlations between the target field and the application fields corresponding to each of the training datasets, dividing the plurality of training datasets into a set of high-correlation training datasets and a set of low-correlation training datasets;
• training the semantic similarity model in turn with training datasets in the set of low-correlation training datasets;
• training the semantic similarity model in turn with the training datasets in the set of high-correlation training datasets.

A non-transitory computer readable storage medium with computer instructions stored thereon, wherein the computer instructions are used for causing a computer to perform a method for training a semantic similarity model for a parameter fine-tuning of a semantic matching task of Natural Language Processing (NLP), the semantic similarity model being configured to receive an input of two texts, calculate the similarity between the two texts, and output a semantic similarity, wherein the method comprises:

• obtaining a target field to be used by a semantic similarity model to be trained;
• calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets;
• training the semantic similarity model with the training datasets in turn, according to an ascending order of the respective correlations between the target field and the application fields corresponding to each of the training datasets, wherein the training the semantic similarity model with the training datasets in turn, according to an ascending order of the respective correlations between the target field and the application fields corresponding to each of the training datasets comprises:
• according to the respective correlations between the target field and the application fields corresponding to each of the training datasets, dividing the plurality of training datasets into a set of high-correlation training datasets and a set of low-correlation training datasets;
• training the semantic similarity model in turn with training datasets in the set of low-correlation training datasets;
• training the semantic similarity model in turn with the training datasets in the set of high-correlation training datasets.