Neural search for programming-related query answering

A non-transitory, computer-readable medium storing program instructions that, when executed using one or more processors, cause the one or more processors to:

• implement an encoder that is initially pre-trained to perform initial embedding of programming-related queries and answers;
• determine contrastive loss values for query-answer pairs, wherein a query of a given query-answer pair is encoded as a vector in Euclidean space and a corresponding answer of the given query-answer pair is encoded as another vector in the Euclidean space, and wherein a contrastive loss value for the given query-answer pair is determined based on comparing a similarity score determined between the encoded query vector and the corresponding encoded answer vector and another similarity score determined between the encoded query vector and an encoded unrelated answer vector;
• train transformer layers for the encoder using the query-answer pairs and the determined contrastive loss values for the respective query-answer pairs such that encoded query vectors are located in the Euclidean space proximate to encoded answer vectors for the respective query-answer pairs, as compared to the encoded unrelated answer vectors;
• and
• provide the encoder comprising the trained transformer layers for performing query answering.

A method, comprising:

• implementing an encoder that is initially pre-trained to perform initial embedding of programming-related queries and answers;
• determining contrastive loss values for query-answer pairs, wherein a query of a given query-answer pair is encoded as a vector in Euclidean space and a corresponding answer of the given query-answer pair is encoded as another vector in the Euclidean space, and wherein a contrastive loss value for the given query-answer pair is determined based on comparing a similarity score determined between the encoded query vector and the corresponding encoded answer vector and another similarity score determined between the encoded query vector and an encoded unrelated answer vector;
• training transformer layers for the encoder using the query-answer pairs and the determined contrastive loss values for the respective query-answer pairs such that encoded query vectors are located in the Euclidean space proximate to encoded answer vectors for the respective query-answer pairs, as compared to the encoded unrelated answer vectors;
• and
• providing the encoder comprising the trained transformer layers for performing query answering.