Intent boundary segmentation for multi-intent utterances

Patent metadata

Field	Value
Publication number	US-11308944-B2
Application number	US-202016816600-A
Country	US
Kind code	B2
Filing date	Mar 12, 2020
Priority date	Mar 12, 2020
Publication date	Apr 19, 2022
Grant date	Apr 19, 2022

Abstract

A mechanism is provided for implementing an intent segmentation mechanism that segments intent boundaries for multi-intent utterances in a conversational agent. For each term of a set of terms in the utterance from a real-time chat session, a set of adversarial utterances is generated for the utterance. An influence of changing each term is determined so as to identify a term importance value. Utilizing the term importance value, one or more of a change in ranking of the intent of the utterance or a change in confidence with regard to the intent of the utterance is identified. An entropy-based segmentation of the utterance into a plurality of candidate partitions is performed. An associated intent and entropy value are then assigned. Based on a segment with minimum entropy, a call associated with the real-time chat session is directed to an operation associated with an intent of the segment with minimum entropy.

First claim

What is claimed is:

1. A method, in a data processing system comprising at least one processor and at least one memory, wherein the at least one memory comprises instructions that are executed by the at least one processor to configure the at least one processor to implement an intent segmentation mechanism that segments intent boundaries for multi-intent utterances in a conversational agent, the method comprising:
responsive to identifying one or more possible intents in an utterance from real-time chat information from a real-time chat session, for each term of a set of terms in the utterance, generating a set of adversarial utterances for the utterance;
determining an influence of changing each term by:
determining a meaning of the utterance without the alternative term;
determining a meaning of the utterance with the alternative term; and
comparing the meaning of the utterance without the alternative term to the meaning of the utterance with the alternative term so as identify a term importance value;

utilizing the term importance value of each term in the set of terms in the utterance, identifying one or more of a change in ranking of the intent of the utterance or a change in confidence with regard to the intent of the utterance;
utilizing the term importance value for each term in the set of terms in the utterance, performing entropy-based segmentation of the utterance into a plurality of candidate partitions;
assigning an associated intent based on ranking and confidence and entropy value to each segment of the utterance based on a sum of term importance values of the terms in the respective segment; and
based on a segment with minimum entropy, directing a call associated with the real-time chat session to an operation associated with the associated intent of the segment with minimum entropy.

2. The method of claim 1 , further comprising:
responsive to completing a portion of the call associated with the intent of the segment with minimum entropy, directing the call associated with the real-time chat session to an further operation associated with an intent of the segment with next minimum entropy.

3. The method of claim 1 , further comprising:
utilizing the identified segmentation along with the identified intent as additional training data for further training of the conversational agent.

4. The method of claim 1 , wherein the one or more possible intents in the utterance from the real-time chat information are identified by the method comprising:
identifying one or more entities within the utterance using natural language processing; and
identifying a possible intent associated with each identified entity in the one or more entities utilizing one or more of semantic classification or text classification.

5. The method of claim 1 , wherein generating the set of adversarial utterances for the utterance performs one or more of replacing terms with paraphrasing terms, randomly sampling replacement words by language models, or utilizing replacement terms that involve typos.

6. The method of claim 1 , wherein generating the set of adversarial utterances for the utterance deletes a word from the utterance.

7. The method of claim 1 , wherein determining the influence of changing each term utilizes the following equations:
Δ L=S before −S after

S
=

∝

1

log
⁡

(

p
⁢
o
⁢
s

+
1

)

+

β
·
confidence

where is a result of a difference of a score before (S before ) the change of the term and a score after (S after ) the change of the term and the evaluation score S considers the effects of both position (pos) in the meaning prediction list for this meaning and the probability (confidence) of the meaning.

8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to implement an abnormal entity detection mechanism that facilitates detecting abnormal entities in real-time response systems through weak supervision, and further causes the data processing system to:
responsive to identifying one or more possible intents in an utterance from real-time chat information from a real-time chat session, for each term of a set of terms in the utterance, generate a set of adversarial utterances for the utterance;
determine an influence of changing each term by:
determining a meaning of the utterance without the alternative term;
determining a meaning of the utterance with the alternative term; and
comparing the meaning of the utterance without the alternative term to the meaning of the utterance with the alternative term so as identify a term importance value;

utilizing the term importance value of each term in the set of terms in the utterance, identify one or more of a change in ranking of the intent of the utterance or a change in confidence with regard to the intent of the utterance;
utilizing the term importance value for each term in the set of terms in the utterance, perform entropy-based segmentation of the utterance into a plurality of candidate partitions;
assign an associated intent based on ranking and confidence and entropy value to each segment of the utterance based on a sum of term importance values of the terms in the respective segment; and
based on a segment with minimum entropy, direct a call associated with the real-time chat session to an operation associated with the associated intent of the segment with minimum entropy.

9. The computer program product of claim 8 , wherein the computer readable program further causes the data processing system to:
responsive to completing a portion of the call associated with the intent of the segment with minimum entropy, direct the call associated with the real-time chat session to an further operation associated with an intent of the segment with next minimum entropy.

10. The computer program product of claim 8 , wherein the computer readable program further causes the data processing system to:
utilize the identified segmentation along with the identified intent as additional training data for further training of the conversational agent.

11. The computer program product of claim 8 , wherein the one or more possible intents in the utterance from the real-time chat information are identified by the computer readable program further causing the data processing system to:
identify one or more entities within the utterance using natural language processing; and
identify a possible intent associated with each identified entity in the one or more entities utilizing one or more of semantic classification or text classification.

12. The computer program product of claim 8 , wherein the computer readable program generates the set of a

Assignees

• IBM

Inventors

• Tan Ming
• Wang Haoyu
• Potdar Saloni
• Yu Yang
• Rao Navneet N
• Qi Haode

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F40/30 Primary

  Semantic analysis · CPC title

• G06F16/90332

  Natural language query formulation or dialogue systems · CPC title

• G10L15/1822

  Parsing for meaning understanding · CPC title

• G10L15/183 Primary

  using context dependencies, e.g. language models · CPC title