Integration of domain information into state transitions of a finite state transducer for natural language processing

• US 10,216,725 B2
• Filed: 02/20/2018
• Issued: 02/26/2019
• Est. Priority Date: 09/16/2014
• Status: Active Grant

• Current Assignee: Cerence Inc.
• Original Assignee: VoiceBox Technologies Corporation (Microsoft Corporation)
• Inventors: Tang, Min
• Primary Examiner(s): Sharma, Neeraj
• Application Number:
  US15/899,389
• Publication Number:
  US 20180239755A1
• Time in Patent Office: 371 Days
• Field of Search: None
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 2015/025 Phonemes, fenemes or fenones being the recognition units

Abstract

The invention relates to a system and method for integrating domain information into state transitions of a Finite State Transducer (“FST”) for natural language processing. A system may integrate semantic parsing and information retrieval from an information domain to generate an FST parser that represents the information domain. The FST parser may include a plurality of FST paths, at least one of which may be used to generate a meaning representation from a natural language input. As such, the system may perform domain-based semantic parsing of a natural language input, generating more robust meaning representations using domain information. The system may be applied to a wide range of natural language applications that use natural language input from a user such as, for example, natural language interfaces to computing systems, communication with robots in natural language, personalized digital assistants, question-answer query systems, and/or other natural language processing applications.

Claims

1. A method being implemented on a computer system having one or more physical processors programmed with computer program instructions to perform the method, the method comprising:

receiving, by the computer system, a text version of one or more words of a natural-language input that includes a natural-language utterance by a user;

obtaining, by the computer system, a semantic grammar that includes word combinations for intent processing and integrates a plurality of domain tokens relating to an information domain, wherein the semantic grammar integrates the plurality of domain tokens structured into a domain information Finite State Transducer (FST) parser that includes at least a first FST path comprising a first set of domain tokens and a second FST path comprising a second set of domain tokens;

matching, by the computer system, the first set of domain tokens and the second set of domain tokens to a first token associated with the utterance;

generating, by the computer system, a first score for the first FST path and a second score for the second FST path based on the matching;

selecting, by the computer system, the first FST path based on the first score and the second score;

determining, by the computer system, a semantic structure of the one or more words based on the selected first FST path; and

generating, by the computer system, a representation of an intention of the user based on the semantic structure, wherein the representation is used to execute a natural-language-based search request or a natural-language-based command.

2. The method of claim 1, wherein the first score is based on a first sum of weights of each domain token among the first set of domain tokens and the second score is based on a second sum of weights of each domain token among the second set of domain tokens, and wherein a given weight for a domain token is based on a level of frequency that the domain token appears in the information domain.

3. The method of claim 1, further comprising:

initializing an input FST based on the first token and the semantic structure; and

composing the input FST based on the first FST path and the second FST path, wherein the first FST path and the second FST path are integrated with the input FST, and wherein the first FST is selected from the input FST.

4. The method of claim 3, wherein selecting the first FST path comprises selecting a shortest path in the input FST.

5. The method of claim 1, wherein the matching comprises:

performing fuzzy or exact matching between the plurality of domain tokens from the information domain and the first token, wherein the plurality of domain tokens comprises fuzzy or exact matches to the first token.

6. The method of claim 1, the method further comprising:

identifying a second token that is relevant to the first token and the information domain, wherein the second token is not initially included in the natural-language input; and

adding the second token to the representation.

7. The method of claim 1, wherein the natural-language input comprises at least a second token, the method further comprising:

determining that the second token is not relevant to the information domain; and

omitting the second token from the representation responsive to the determination that the second token is not relevant.

8. The method of claim 1, the method further comprising:

obtaining a phoneme confusion matrix comprising at least two similar sounding words that are disambiguated based on previous training from one or more user utterances; and

disambiguating the first token based on the phoneme confusion matrix.

9. The method of claim 1, the method further comprising:

obtaining one or more dynamic data tokens from a dynamic data source; and

integrating the one or more dynamic data tokens with the plurality of tokens from the information domain, wherein the representation is determined based on the integrated dynamic data tokens.

10. The method of claim 9, wherein the plurality of domain tokens are structured into a domain information FST parser that includes at least a first FST path comprising a first set of domain tokens and a second FST path comprising a second set of domain tokens, and wherein integrating the one or more dynamic data tokens comprises:

generating a dynamic FST based on the one or more dynamic data tokens; and

inserting the dynamic FST into a slot of the domain information FST parser reserved for dynamic data.

11. The method of claim 1, wherein the information domain comprises a plurality of entries of searchable information, and wherein the matching comprises:

determining at least one entry, which includes the plurality of domain tokens, that is likely being searched for based on the first token.

23. The method of claim 1, wherein the first FST path is associated with a first action to be performed and the second FST path is associated with a second action to be performed, the method further comprising:

recognizing an action to be performed based on one or more tokens of the one or more words, wherein the first FST path is selected based further on the recognized action to be performed and the first action to be performed.

24. The system of claim 3, wherein the first FST path is associated with a first action to be performed and the second FST path is associated with a second action to be performed, the computer system is further programmed to:

recognize an action to be performed based on one or more tokens of the one or more words, wherein the first FST path is selected based further on the recognized action to be performed and the first action to be performed.

12. A system comprising:

a computer system comprising one or more physical processors programmed with computer program instructions to;

receive a text version of one or more words of a natural-language input that includes a natural-language utterance by a user;

obtain a semantic grammar that includes word combinations for intent processing and integrates a plurality of domain tokens relating to an information domain, wherein the semantic grammar integrates the plurality of domain tokens structured into a domain information Finite State Transducer (FST) parser that includes at least a first FST path comprising a first set of domain tokens and a second FST path comprising a second set of domain tokens;

match the first set of domain tokens and the second set of domain tokens to a first token associated with the utterance;

generate a first score for the first FST path and a second score for the second FST path based on the match;

select the first FST path based on the first score and the second score;

determine a semantic structure of the one or more words based on the selected first FST path; and

generate a representation of an intention of the user based on the semantic structure, wherein the representation is used to execute a natural-language-based search request or a natural-language-based command.

13. The system of claim 12, wherein the first score is based on a first sum of weights of each domain token among the first set of domain tokens and the second score is based on a second sum of weights of each domain token among the second set of domain tokens, and wherein a given weight for a domain token is based on a level of frequency that the domain token appears in the information domain.

14. The system of claim 12, wherein the computer system is further programmed to:

initialize an input FST based on the first token and the semantic structure; and

compose the input FST based on the first FST path and the second FST path, wherein the first FST path and the second FST path are integrated with the input FST, and wherein the first FST is selected from the input FST.

15. The system of claim 14, wherein to select the first FST path, the computer system is further programmed to:

select a shortest path in the input FST.

16. The system of claim 12, wherein to perform the match, the computer system is further programmed to:

perform fuzzy or exact matching between the plurality of domain tokens from the information domain and the first token, wherein the plurality of domain tokens comprises fuzzy or exact matches to the first token.

17. The system of claim 12, wherein the computer system is further programmed to:

identify a second token that is relevant to the first token and the information domain, wherein the second token is not initially included in the natural-language input; and

add the second token to the representation.

18. The system of claim 12, wherein the natural-language input comprises at least a second token, and wherein the computer system is further programmed to:

determine that the second token is not relevant to the information domain; and

omit the second token from the representation responsive to the determination that the second token is not relevant.

19. The system of claim 12, wherein the computer system is further programmed to:

obtain a phoneme confusion matrix comprising at least two similar sounding words that are disambiguated based on previous training from one or more user utterances; and

disambiguate the first token based on the phoneme confusion matrix.

20. The system of claim 12, wherein the computer system is further programmed to:

obtain one or more dynamic data tokens from a dynamic data source; and

integrate the one or more dynamic data tokens with the plurality of tokens from the information domain, wherein the representation is determined based on the integrated dynamic data tokens.

21. The system of claim 20, wherein the plurality of domain tokens are structured into a domain information FST parser that includes at least a first FST path comprising a first set of domain tokens and a second FST path comprising a second set of domain tokens, and wherein to integrate the one or more dynamic data tokens, the computer system is further programmed to:

generate a dynamic FST based on the one or more dynamic data tokens; and

insert the dynamic FST into a slot of the domain information FST parser reserved for dynamic data.

22. The system of claim 12, wherein the information domain comprises a plurality of entries of searchable information, and wherein to perform the match, the computer system is further programmed to:

determine at least one entry, which includes the plurality of domain tokens, that is likely being searched for based on the first token.