Systems and methods for determining a response to a user query

1. A system comprising:

a non-transitory memory having program instructions stored thereon; and

one or more hardware processors coupled to the non-transitory memory and configured to execute the stored instructions to cause the system to perform operations comprising:

detecting reception of a user query by a chatbot;

utilizing an augmentation machine learning model to determine multiple different variations of the user query that are semantically related to the user query;

storing the multiple different variations of the user query in a first asynchronous decoupling buffer;

accessing a set of documents, wherein a given document includes one or more query-response pairs, including:

querying the set of documents by comparing queries of query-response pairs in the documents with both:

the user query; and

the multiple different variations of the user query from the first asynchronous decoupling buffer;

determining a plurality of response candidates based on the querying and storing the plurality of response candidates in a second asynchronous decoupling buffer;

determining a final response candidate from the plurality of response candidates from the second asynchronous decoupling buffer, wherein the determining utilizes a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query; and

outputting, by the chatbot, a response to the user based on the determined final response candidate.

2. The system of claim 1 , wherein determining the plurality of response candidates that correspond to the user query is based on a term frequency-inverse document frequency process comparing the user query and the multiple different variations of the user query to the set of documents.

3. The system of claim 1 , wherein the operations further comprise:

monitoring user browsing activity; and

generating the user query and directing the user query to the chatbot based on the browsing activity.

4. The system of claim 1 ,

wherein the utilizing the augmentation machine learning model includes:

performing a most-similar word search to generate words similar to words in the user query;

mapping the user query and results of the most-similar word search to a vector space;

performing a distributed word-embedding technique to compare learned representations of semantically-related words as multi-dimensional vectors in the vector space; and

outputting the multiple different variations based on the distributed word-embedding;

wherein the utilizing of the semantic machine learning model includes:

providing token sequences that include tokens from the user query and tokens from a given response candidate;

processing the token sequences using a Bidirectional Encoder Representations from Transformers (BERT) model to generate score values that reflects a semantic relationship between the user query and a given response candidate;

generating an initial ranking of the score values;

adjust the initial ranking, using a ranking loss model, to generate a final ranking, wherein the determining the response candidate is based on the final ranking; and

propagating the final ranking to the BERT model.

5. The system of claim 1 , wherein the determining the final response candidate includes:

determining scores for the plurality of response candidates, and

identifying the response candidate having the highest score as the final response candidate.

6. The system of claim 5 , wherein, when determining scores for the plurality of response candidates, semantic similarities of the response candidates to the user query are weighted more than syntactic similarities of the response candidates to the user query.

7. The system of claim 1 , wherein the first asynchronous decoupling buffer is a push/pull buffer.

8. The system of claim 1 , wherein the operations further comprise utilizing one or more normalization techniques to normalize the user query; and

wherein the augmentation machine learning model determines the multiple different variations of the user query that correspond to a semantic meaning of the normalized user query.

9. The system of claim 8 , wherein utilizing one or more normalization techniques to normalize the user query includes utilizing a text normalization machine learning model to normalize the user query.

10. A computer-implemented method comprising:

detecting, by a computing system, reception of a user query by a chatbot;

utilizing, by the computing system, an augmentation machine learning model to determine multiple different variations of the user query that are semantically related to the user query;

storing, by the computing system, the multiple different variations of the user query in a first asynchronous decoupling buffer;

accessing, by the computing system, a set of documents, wherein a given document includes one or more query-response pairs, including:

querying, by the computing system, the set of documents by comparing queries of query-response pairs in the documents with both:

the user query; and

the multiple different variations of the user query from the first asynchronous decoupling buffer;

determining, by the computing system, a plurality of response candidates based on the querying and storing the plurality of response candidates in a second asynchronous decoupling buffer;

determining, by the computing system, a final response candidate from the plurality of response candidates from the second asynchronous decoupling buffer, wherein the determining utilizes a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query; and

outputting, by the chatbot, a response to the user based on the determined final response candidate.

11. The computer-implemented method of claim 10 , wherein determining the plurality of response candidates that correspond to the user query is based on a term frequency-inverse document frequency process to compare the user query and the multiple different variations of the user query to the set of documents.

12. The computer-implemented method of claim 10 , wherein the user query and the multiple different variations of the user query are syntactically different and semantically similar.

13. The computer-implemented method of claim 10 , wherein the determining the final response candidate includes:

determining scores for the plurality of response candidates, and

identifying the response candidate having the highest score as the final response candidate.

14. The computer-implemented method of claim 10 , further comprising:

monitoring, by the system, user browsing activity; and

generating, by the system, the user query and directing the user query to the chatbot based on the browsing activity.

15. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

detecting reception of a user query by a chatbot;

utilizing an augmentation machine learning model to determine multiple different variations of the user query that are semantically related to correspond to a semantic meaning of the user query;

storing the multiple different variations of the user query in a first asynchronous decoupling buffer;

accessing a set of documents, wherein a given document includes one or more query-response pairs, including:

querying the set of documents by comparing queries of query-response pairs in the documents with both:

the user query; and

the multiple different variations of the user query from the first asynchronous decoupling buffer;

determining a plurality of response candidates based on the querying and storing the plurality of response candidates in a second asynchronous decoupling buffer that correspond to the user query by comparing the user query and the multiple different variations of the user query to a plurality of documents;

determining a final response candidate from the plurality of response candidates from the second asynchronous decoupling buffer, wherein the determining utilizes a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query; and

outputting, by the chatbot, a response to the user based on the determined final response candidate.

16. The non-transitory machine-readable medium of claim 15 , wherein the determining the plurality of response candidates that correspond to the user query is based on a term frequency-inverse document frequency process to compare the user query and the multiple different variations of the user query to the plurality of documents.

17. The non-transitory machine-readable medium of claim 15 , wherein the user query and the multiple different variations of the user query are syntactically different and semantically similar.

18. The non-transitory machine-readable medium of claim 15 , wherein the determining the final response candidate comprises determining scores for the plurality of response candidates, the response candidate having the highest score being determined as the final response candidate.

19. The non-transitory machine-readable medium of claim 15 , wherein the operations further comprise:

utilizing one or more normalization techniques to normalize the user query; and

wherein the augmentation machine learning model determines the multiple different variations of the user query that correspond to a semantic meaning of the normalized user query.

20. The non-transitory machine-readable medium of claim 19 , wherein utilizing one or more normalization techniques to normalize the user query includes utilizing a text normalization machine learning model to normalize the user query.