Generating canonical forms for task-oriented dialogue in conversational ai systems and applications

Patent metadata

Field	Value
Publication number	US-2024062014-A1
Application number	US-202217889124-A
Country	US
Kind code	A1
Filing date	Aug 16, 2022
Priority date	Aug 16, 2022
Publication date	Feb 22, 2024
Grant date	—

Abstract

In various examples, techniques for training and using a task-oriented dialogue system are described. Systems and methods are disclosed for determining, using a prompt model(s) and based at least in part on text data, prompt data representing one or more prompts. Additionally, systems and method are disclosed for determining, using a language model(s) and based at least in part on the text data and the prompt data, a canonical form associated with the text data. In some examples, the prompt model(s) is trained to generate the prompt data that causes the language model(s) to output the canonical form. Systems and method are further disclosed for using the canonical form to determine at least an intent associated with the text data. A dialogue manager may then use the intent to perform one or more actions associated with the text data.

First claim

What is claimed is:

1 . A method comprising:
determining, using one or more first models and based at least in part on text data representing text, data representing one or more prompts; determining, using one or more second models and based at least in part on the text data and the data representing one or more prompts, a canonical form associated with the text; and determining, based at least in part on the canonical form, at least an intent associated with the text.

2 . The method of claim 1 , wherein the data representing one or more prompts comprises data representing one or more vectors associated with the one or more prompts.

3 . The method of claim 1 , wherein the determining the canonical representation associated with the text comprises:
determining, using the one or more second models and based at least on the text data and the data representing one or more prompts, one or more vectors associated with one or more words; determining a final vector based at least in part on the one or more vectors; and determining that the final vector is associated with the canonical form.

4 . The method of claim 3 , wherein the determining that the final vector is associated with the canonical form comprises:
comparing the final vector to a set of one or more vectors, wherein at least one individual vector of the set of one or more vectors is associated with a respective canonical form associated with the text; determining, based at least on the comparing, that the final vector is similar to a vector from the set of one or more vectors; and determining that the vector is associated with the canonical form.

5 . The method of claim 1 , further comprising:
determining, based at least on at least one of the text data or the data representing one or more prompts, information associated with one or more slots for the intent; and inputting the intent and the information associated with the one or more slots into a dialogue manager.

6 . The method of claim 1 , further comprising:
determining, using the one or more first models and based at least on second text data representing second text, second data representing one or more prompts, wherein the text is different from the second text; and determining, using the one or more second models and based at least on the second text data and the second data representing one or more prompts, the canonical form is associated with the second text.

7 . The method of claim 1 , wherein:
the canonical form is associated with the intent; and the one or more first models are trained to output the data representing one or more prompts that the one or more second models use to determine the canonical form associated with the intent.

8 . The method of claim 1 , further comprising:
determining, using the one or more first models and based at least in part on second text data representing second text, second data representing one or more prompts; determining, using the one or more second models and based at least on the second text data and the second data representing one or more prompts, a second canonical form associated with the second text; and updating one or more parameters associated with the one or more first models based at least in part on the second canonical form and ground truth data associated with the second text data, the ground truth data indicating that the second text data is associated with the canonical form.

9 . A processor comprising:
one or more processing units to perform operations comprising:
determining, using one or more prompt models and based at least on text data representing text, prompt data representing one or more prompts;
determining, using one or more language models and based at least on the text data and the prompt data, a first canonical form associated with the text; and
updating one or more parameters associated with the one or more prompt models based at least on ground truth data associated with the text data indicating that the text corresponds to a second canonical form different from the first canonical form.

10 . The processor of claim 9 , wherein the updating the one or more parameters associated with the one or more prompt models comprises:
determining an error based at least on the first canonical form and the second canonical form; and updating the one or more parameters based at least on the error.

11 . The processor of claim 9 , wherein the prompt data represents one or more vectors associated with the one or more prompts.

12 . The processor of claim 9 , wherein the determining the first canonical representation associated with the text comprises:
determining, using the one or more language models and based at least on the text data and the prompt data, one or more vectors associated with one or more words; determining a final vector based at least on the one or more vectors; and determining that the final vector is associated with the first canonical form.

13 . The processor of claim 12 , wherein the determining that the final vector is associated with the first canonical form comprises:
comparing the final vector to a set of one or more vectors, wherein at least one individual vector from the set of one or more vectors is associated with a respective canonical form; determining, based at least on the comparing, that the final vector is similar to a vector from the set of one or more vectors; and determining that the vector is associated with the first canonical form.

14 . The processor of claim 9 , wherein the operations further comprise:
determining, using the one or more prompt models and based at least on second text data representing second text, second prompt data representing one or more second prompts; determining, using the one or more language models and based at least on the second text data and the second prompt data, a third canonical form associated with the second text; and updating the one or more parameters associated with the one or more prompt models based at least on second ground truth data associated with the second text data indicating that the second text corresponds to the second canonical form different from the third canonical form.

15 . The processor of claim 9 , wherein the ground truth data for the updating the one or more parameters of the one or more prompt models corresponds to one or more outputs of the one or more language models.

16 . A system comprising:
one or more processing units to:
generate, using one or more prompt models and based at least on data representing a textual input, one or more outputs;
determine, using one or more language models and based at least on the one or more outputs, a canonical representation of the textual input; and
determine, based at least on the canonical representation, an intent associated with the textual input.

17 . The system of claim 16 , wherein the intent is determined based at least on comparing the canonical representation to a set of one or more canonical representations associated with a set of one or more intents, and the intent corresponds to an individual canonical representation of the set of one or more canonical representation that is most similar to the canonical representation.

18 . The system of claim 16 , wherein the canonical representation associated with the textual input is represented using one or more first vectors, and the intent is determined based at least on comparing the one or more first vectors to one or more second vectors corresponding to one or more intents.

19 .

Assignees

• Nvidia Corp

Inventors

• Sreedhar Makesh Narsimhan
• Parisien Christopher

Classifications

• G06F40/30 Primary

  Semantic analysis · CPC title

• G10L15/22

  Procedures used during a speech recognition process, e.g. man-machine dialogue · CPC title

• G06F40/35 Primary

  Discourse or dialogue representation · CPC title

• G06F16/3329

  Natural language query formulation · CPC title

• G06F40/126

  Character encoding · CPC title