Intent Matching Natural Language Queries
A server accesses a natural language query. The server facilitates a mapping of the natural language query to a vector using a query-to-vector engine. The server matches the vector to an intent representing a prediction associated with the natural language query. The server provides a response to the natural language query based on the intent.
1 . A method, comprising:
facilitating a mapping of a natural language query to a vector using a query-to-vector engine;
matching the vector to an intent using a vector-to-intent engine trained by locking word embeddings; and
providing a response to the natural language query based on the intent.
2 . The method of claim 1 , comprising:
testing the query-to-vector engine and the vector-to-intent engine by verifying that a first query in a first natural language matches to a same intent as a translation of the first query into a second natural language.
3 . The method of claim 1 , wherein the query-to-vector engine is configured to leverage the word embeddings in each of a plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries.
4 . The method of claim 1 , wherein the intent represents a grouping of a set of queries, including the natural language query, for further processing.
5 . The method of claim 1 , wherein the vector is a numeric vector in a multi-dimensional space.
6 . The method of claim 1 , wherein facilitating the mapping of the natural language query to the vector does not include translating the natural language query into a natural language different from a natural language of the natural language query.
7 . The method of claim 1 , wherein the vector is matched to the intent using a machine learning technique.
8 . Non-transitory computer readable media storing instructions operable to cause one or more processors to perform operations comprising:
facilitating a mapping of a natural language query to a vector using a query-to-vector engine;
matching the vector to an intent using a vector-to-intent engine trained by locking word embeddings; and
providing a response to the natural language query based on the intent.
9 . The non-transitory computer readable media of claim 8 , the operations comprising:
testing the query-to-vector engine and the vector-to-intent engine by verifying that a first query in a first natural language matches to a same intent as a translation of the first query into a second natural language.
10 . The non-transitory computer readable media of claim 8 , wherein the query-to-vector engine is configured to leverage the word embeddings in each of a plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries.
11 . The non-transitory computer readable media of claim 8 , wherein the intent represents a grouping of a set of queries, including the natural language query, for further processing.
12 . The non-transitory computer readable media of claim 8 , wherein the vector is a numeric vector in a multi-dimensional space.
13 . The non-transitory computer readable media of claim 8 , wherein facilitating the mapping of the natural language query to the vector does not include translating the natural language query into a natural language different from a natural language of the natural language query.
14 . The non-transitory computer readable media of claim 8 , wherein the vector is matched to the intent using a machine learning technique.
15 . A system comprising:
memory hardware; and
one or more processors configured to execute instructions stored in the memory hardware to:
facilitate a mapping of a natural language query to a vector using a query-to-vector engine;
match the vector to an intent using a vector-to-intent engine trained by locking word embeddings; and
provide a response to the natural language query based on the intent.
16 . The system of claim 15 , the one or more processors configured to execute the instructions stored in the memory hardware to:
test the query-to-vector engine and the vector-to-intent engine by verifying that a first query in a first natural language matches to a same intent as a translation of the first query into a second natural language.
17 . The system of claim 15 , wherein the query-to-vector engine is configured to leverage the word embeddings in each of a plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries.
18 . The system of claim 15 , wherein the intent represents a grouping of a set of queries, including the natural language query, for further processing.
19 . The system of claim 15 , wherein the vector is a numeric vector in a multi-dimensional space.
20 . The system of claim 15 , wherein facilitating the mapping of the natural language query to the vector does not include translating the natural language query into a natural language different from a natural language of the natural language query.