Statistical Learning from Dialogues for Intelligent Assistants

DR. YUN-NUNG (VIVIAN) CHEN H T T P : / / V I V I A N C H E N . I D V.T W
Statistical Learning from Dialogues for Intelligence Assistants
Sorry, I didn’t get that!
1"SORRY, I DIDN'T GET THAT!" -- STATISTICAL LEARNING FROM DIALOGUES FOR INTELLIGENT ASSISTANTS

My Background
Yun-Nung (Vivian) Chen 陳縕儂 http://vivianchen.idv.tw
National Taiwan
University
2009
B.S.
2005
Freshman
2011
M.S.
2015
Ph.D.
Carnegie Mellon
University
spoken dialogue system
language understanding
user modeling
speech summarization
key term extraction
spoken term detection
Microsoft
Research
2016
Postdoc
"SORRY, I DIDN'T GET THAT!" -- STATISTICAL LEARNING FROM DIALOGUES FOR INTELLIGENT ASSISTANTS 2

Outline
Intelligent Assistant
◦ What are they?
◦ Why do we need them?
◦ Why do companies care?
Reactive Assistant – Spoken Dialogue System (SDS)
◦ Pipeline Architecture
◦ Current Challenges & Overview Contributions
Semantic Decoding
Intent Prediction
Conclusions & Future Work

Outline
◦ What are they?
Semantic Decoding
Intent Prediction

Apple Siri
(2011)
Google Now
(2012)
Microsoft Cortana
(2014)
Amazon Alexa/Echo
(2014)
https://www.apple.com/ios/siri/
https://www.google.com/landing/now/
http://www.windowsphone.com/en-us/how-to/wp8/cortana/meet-cortana
http://www.amazon.com/oc/echo/
Facebook M
(2015)
What are Intelligent Assistants?

Why do we need them?
Daily Life Usage
◦ Weather
◦ Schedule
◦ Transportation
◦ Restaurant Seeking

Why do we need them?
◦ Get things done
◦ E.g. set up alarm/reminder, take note
◦ Easy access to structured data, services and apps
◦ E.g. find docs/photos/restaurants
◦ Assist your daily schedule and routine
◦ E.g. commute alerts to/from work
◦ Be more productive in managing your work and personal life

Why do companies care?
Global Digital Statistics (2015 January)
Global Population
7.21B
Active Internet Users
3.01B
Active Social
Media Accounts
2.08B
Active Unique
Mobile Users
3.65B
The more natural and convenient input of the devices evolves towards speech.

Personal Intelligent Architecture
Reactive
Assistance
ASR, LU, Dialog, LG, TTS
Proactive
Assistance
Inferences, User
Modeling, Suggestions
Data
Back-end Data
Bases, Services and
Client Signals
Device/Service End-points
(Phone, PC, Xbox, Web Browser, Messaging Apps)
User Experience
“restaurant suggestions”“call taxi”

Reactive
Assistance
Proactive
Assistance
Inferences, User
Data
Back-end Data
Bases, Services and
Client Signals
User Experience
“call taxi”

Outline
◦ What are they?
Semantic Decoding
Intent Prediction

Spoken dialogue systems are intelligent agents that are able to help
users finish tasks more efficiently via spoken interactions.
Spoken dialogue systems are being incorporated into various devices
(smart-phones, smart TVs, in-car navigating system, etc).
Good SDSs assist users to organize and access information conveniently.
Spoken Dialogue System (SDS)
JARVIS – Iron Man’s Personal Assistant Baymax – Personal Healthcare Companion

Baymax is capable of maintaining a good spoken dialogue system and learning new
knowledge for better understanding and interacting with people.
What is Baymax’s intelligence?
Big Hero 6 -- Video content owned and licensed by Disney Entertainment, Marvel Entertainment, LLC, etc

ASR: Automatic Speech Recognition
SLU: Spoken Language Understanding
DM: Dialogue Management
NLG: Natural Language Generation
SDS Architecture
DomainDMASR SLU
NLG
current
bottleneck

Interaction Example
User
Intelligent
Agent Q: How does a dialogue system process this request?
Cheap Taiwanese eating places include Din Tai
Fung, Boiling Point, etc. What do you want to
choose? I can help you go there.
find a cheap eating place for taiwanese food

SDS Process – Available Domain Ontology
User
target
foodprice
AMOD
NN
seeking
PREP_FOR
Organized Domain Knowledge
Intelligent
Agent

User
target
foodprice
AMOD
NN
seeking
PREP_FOR
Intelligent
Agent
Ontology Induction
(semantic slot)

User
target
foodprice
AMOD
NN
seeking
PREP_FOR
Intelligent
Agent
Ontology Induction
(semantic slot)
Structure Learning
(inter-slot relation)

SDS Process – Spoken Language Understanding (SLU)
User
target
foodprice
AMOD
NN
seeking
PREP_FOR
Intelligent
Agent
seeking=“find”
target=“eating place”
price=“cheap”
food=“taiwanese”

SDS Process – Spoken Language Understanding (SLU)
User
target
foodprice
AMOD
NN
seeking
PREP_FOR
Intelligent
Agent
seeking=“find”
price=“cheap”
food=“taiwanese”
Semantic Decoding

SDS Process – Dialogue Management (DM)
User
target
foodprice
AMOD
NN
seeking
PREP_FOR
SELECT restaurant {
restaurant.price=“cheap”
restaurant.food=“taiwanese”
}Intelligent
Agent

User
target
foodprice
AMOD
NN
seeking
PREP_FOR
SELECT restaurant {
}Intelligent
Agent
Surface Form Derivation
(natural language)

User
SELECT restaurant {
}
Din Tai Fung
Boiling Point
:
:
Predicted intent: navigation
Intelligent
Agent

User
SELECT restaurant {
}
Din Tai Fung
Boiling Point
:
:
Intelligent
Agent
Intent Prediction

SDS Process – Natural Language Generation (NLG)
User
Intelligent
Agent
Cheap Taiwanese eating places include Din Tai
Fung, Boiling Point, etc. What do you want to
choose? I can help you go there. (navigation)

Required Knowledge
target
foodprice
AMOD
NN
seeking
PREP_FOR
SELECT restaurant {
}
User
Required Domain-Specific Information

Challenges for SDS
An SDS in a new domain requires
1) A hand-crafted domain ontology
2) Utterances labelled with semantic representations
3) An SLU component for mapping utterances into semantic representations
Manual work results in high cost, long duration and poor scalability of
system development.
The goal is to enable an SDS to
1) automatically infer domain knowledge and then to
2) create the data for SLU modeling
in order to handle the open-domain requests.
seeking=“find”
price=“cheap”
food=“asian food”
find a cheap eating
place for asian food 
 fully unsupervised
Prior
Focus

Contributions
target
foodprice
AMOD
NN
seeking
PREP_FOR
SELECT restaurant {
restaurant.food=“asian food”
}
User
Ontology Induction
Structure Learning
Semantic Decoding
Intent Prediction
(natural language)
(inter-slot relation)
(semantic slot)

Contributions
User
Ontology Induction
Structure Learning
Semantic Decoding
Intent Prediction

 Ontology Induction
 Structure Learning
 Surface Form Derivation
 Semantic Decoding
 Intent Prediction
Contributions
User
Knowledge Acquisition SLU Modeling

Knowledge Acquisition
1) Given unlabelled conversations, how can a system automatically
induce and organize domain-specific concepts?
Restaurant
Asking
Conversations
target
food
price
seeking
quantity
PREP_FOR
PREP_FOR
NN AMOD
AMOD
AMOD
Organized Domain
Knowledge
Unlabelled
Collection
Knowledge
Acquisition
Knowledge Acquisition  Ontology Induction
 Structure Learning
 Surface Form Derivation

SLU Modeling
2) With the automatically acquired knowledge, how can a system
understand utterance semantics and user intents?
Organized
Domain
Knowledge
price=“cheap”
target=“restaurant”
intent=navigation
SLU Modeling
SLU Component
“can i have a cheap restaurant”
SLU Modeling
 Semantic Decoding
 Intent Prediction

SDS Architecture – Contributions
DomainDMASR SLU
NLG
Knowledge Acquisition SLU Modeling
current
bottleneck

SDS Flowchart
Ontology
Induction
Structure
Learning
Semantic
Decoding
Intent
Prediction
SLU Modeling

SDS Flowchart – Semantic Decoding
Ontology
Induction
Structure
Learning
Semantic
Decoding
Intent
Prediction
SLU Modeling

Outline
◦ What are they?
Semantic Decoding
Intent Prediction

Semantic Decoding [ACL-IJCNLP’15]
Input: user utterances
Output: semantic concepts included in each individual utterance
Chen et al., "Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding," in Proc. of ACL-IJCNLP, 2015.
SLU Model
price=“cheap”
“can I have a cheap restaurant”
Frame-Semantic Parsing
Unlabeled
Collection
Semantic KG
Ontology Induction
Fw Fs
Feature Model
Rw
Rs
Knowledge Graph
Propagation Model
Word Relation Model
Lexical KG
Slot Relation Model
Structure
Learning
×
Semantic KG
MF-SLU: SLU Modeling by Matrix Factorization
Semantic Representation

[Baker et al. 1998; Das et al., 2014]
FrameNet [Baker et al., 1998]
◦ a linguistically semantic resource, based on the frame-semantics theory
◦ words/phrases can be represented as frames
◦ “low fat milk”  “milk” evokes the “food” frame;
“low fat” fills the descriptor frame element
SEMAFOR [Das et al., 2014]
◦ a state-of-the-art frame-semantics parser, trained on manually annotated
FrameNet sentences

Ontology Induction [ASRU’13, SLT’14a]
can i have a cheap restaurant
Frame: capability
Frame: expensiveness
Frame: locale by use
1st Issue: differentiate domain-specific frames from generic frames for SDSs
Good!
Good!
?
Das et al., " Frame-semantic parsing," in Proc. of Computational Linguistics, 2014.
slot candidate
Best Student Paper Award

1
Utterance 1
i would like a cheap restaurant
Train
………
cheap restaurant foodexpensiveness
1
locale_by_use
11
find a restaurant with chinese food
Utterance 2
1 1
food
1 1
1
Test
1 .97 .95
Frame Semantic Parsing
show me a list of cheap restaurants
Test Utterance
Word Observation Slot Candidate
Ontology Induction [ASRU’13, SLT’14a]
Best Student Paper Award
Idea: increase weights of domain-specific slots and decrease weights of others

1st Issue: How to adapt generic slots to a domain-specific setting?
Knowledge Graph Propagation Model
Assumption: domain-specific words/slots have more dependencies to each other
Word Relation Model Slot Relation Model
word
relation
matrix
slot
relation
matrix
×
1
Train
1
locale_by_use
11
1 1
food
1 1
1
Test
1
1
Slot Induction
Relation matrices allow nodes to propagate scores to their neighbors
in the knowledge graph, so that domain-specific words/slots have
higher scores after matrix multiplication.
i like
1 1
capability
1
locale_by_use
food expensiveness
seeking
relational_quantitydesiring
Utterance 1
……
Utterance 2
Test Utterance

SLU Model
price=“cheap”
Unlabeled
Collection
Semantic KG
Ontology Induction
Fw Fs
Feature Model
Rw
Rs
Knowledge Graph
Propagation Model
Word Relation Model
Lexical KG
Slot Relation Model
Structure
Learning
×
Semantic KG

Knowledge Graph Construction
Syntactic dependency parsing on utterances
ccomp
amod
dobjnsubj det
capability expensiveness locale_by_use
Word-based lexical
knowledge graph
Slot-based semantic
knowledge graph
restaurant
can
have
i
a
cheap
w
w
capability
locale_by_use expensiveness
s

Dependency-based word embeddings
Dependency-based slot embeddings
Edge Weight Measurement
Slot/Word Embeddings Training (Levy and Goldberg, 2014)
can = [0.8 … 0.24]
have = [0.3 … 0.21]
:
:
expensiveness = [0.12 … 0.7]
capability = [0.3 … 0.6]
:
:
ccomp
amod
dobjnsubj det
have acapability expensiveness locale_by_use
ccomp
amod
dobjnsubj det
Levy and Goldberg, " Dependency-Based Word Embeddings," in Proc. of ACL, 2014.

Edge Weight Measurement
Compute edge weights to represent relation importance
◦ Slot-to-slot semantic relation 𝑅 𝑠
𝑆
: similarity between slot embeddings
◦ Slot-to-slot dependency relation 𝑅 𝑠
𝐷
: dependency score between slot embeddings
◦ Word-to-word semantic relation 𝑅 𝑤
𝑆
: similarity between word embeddings
◦ Word-to-word dependency relation 𝑅 𝑤
𝐷
: dependency score between word embeddings
𝑅 𝑤
𝑆𝐷
= 𝑅 𝑤
𝑆
+𝑅 𝑤
𝐷
𝑅 𝑠
𝑆𝐷 = 𝑅 𝑠
𝑆+𝑅 𝑠
𝐷
w1
w2
w3
w4
w5
w6
w7
s2
s1 s3

word
relation
matrix
slot
relation
matrix
×
1
Train
1
locale_by_use
11
1 1
food
1 1
1
Test
1
1
Slot Induction
𝑅 𝑤
𝑆𝐷
𝑅 𝑠
𝑆𝐷
Structure information is integrated to make the self-training data more reliable.

Ontology Induction
SLU
Fw Fs
Structure
Learning
×
1
Utterance 1
Train
…
1
locale_by_use
11
Utterance 2
1 1
food
1 1
1
Test
1 .97.90 .95.85
Ontology
Induction
Test Utterance hidden semantics
2nd Issue: unobserved semantics may benefit understanding

Reasoning with Matrix Factorization
word
relation
matrix
slot
relation
matrix
×
1
Train
1
locale_by_use
11
1 1
food
1 1
1
Test
1
1
.97.90 .95.85
.93 .92.98.05 .05
Slot Induction
Feature Model +
𝑅 𝑤
𝑆𝐷
𝑅 𝑠
𝑆𝐷
Idea: MF completes a partially-missing matrix based on a low-rank latent semantics
assumption, which is able to model hidden semantics and more robust to noisy data.

2nd Issue: How to model the unobserved hidden semantics?
Matrix Factorization (MF) (Rendle et al., 2009)
The decomposed matrices represent latent semantics for utterances
and words/slots respectively
The product of two matrices fills the probability of hidden semantics
1
Train
1
locale_by_use
11
1 1
food
1 1
1
Test
1
1
.97.90 .95.85
.93 .92.98.05 .05
𝑼
𝑾 + 𝑺
≈ 𝑼 × 𝒅 𝒅 × 𝑾 + 𝑺×
Rendle et al., “BPR: Bayesian Personalized Ranking from Implicit Feedback," in Proc. of UAI, 2009.

Bayesian Personalized Ranking for MF
Model implicit feedback
◦ not treat unobserved facts as negative samples (true or false)
◦ give observed facts higher scores than unobserved facts
Objective:
1
𝑓+
𝑓−
𝑓−
The objective is to learn a set of well-ranked semantic slots per utterance.
𝑢
𝑥

Ontology Induction
SLU
Fw Fs
Structure
Learning
×
1
Utterance 1
Train
…
1
locale_by_use
11
Utterance 2
1 1
food
1 1
1
Test
1 .97.90 .95.85
Ontology
Induction
Test Utterance
Matrix Factorization SLU (MF-SLU)
MF-SLU can estimate probabilities for slot candidates given test utterances.

SLU Model
price=“cheap”
Unlabeled
Collection
Semantic KG
Ontology Induction
Fw Fs
Feature Model
Rw
Rs
Knowledge Graph
Propagation Model
Word Relation Model
Lexical KG
Slot Relation Model
Structure
Learning
×
Semantic KG
Idea: utilize the acquired knowledge to decode utterance semantics (fully unsupervised)

Experimental Setup
Dataset: Cambridge University SLU Corpus
◦ Restaurant recommendation (WER = 37%)
◦ 2,166 dialogues
◦ 15,453 utterances
◦ dialogue slot:
addr, area, food, name,
phone, postcode,
price range, task, type
Metric: MAP of all estimated slot probabilities over all utterances
The mapping table between induced and reference slots
Henderson et al., "Discriminative spoken language understanding using word confusion networks," in Proc. of SLT, 2012.

Experiments of Semantic Decoding
Quality of Semantics Estimation
Metric: MAP of all estimated slot probabilities for all utterances
Approach ASR Transcripts
Baseline:
SLU
Support Vector Machine 32.5 36.6
Multinomial Logistic Regression 34.0 38.8

Quality of Semantics Estimation
The MF-SLU effectively models implicit information to decode semantics.
The structure information further improves the results.
Baseline:
SLU
Support Vector Machine 32.5 36.6
Multinomial Logistic Regression 34.0 38.8
Proposed:
MF-SLU
Feature Model 37.6* 45.3*
Feature Model +
Knowledge Graph Propagation
43.5*
(+27.9%)
53.4*
(+37.6%)
*: the result is significantly better than the MLR with p < 0.05 in t-test

Effectiveness of Relations
In the integrated structure information, both semantic and dependency
relations are useful for understanding.
Feature Model 37.6 45.3
Feature + Knowledge
Graph Propagation
Semantic 41.4* 51.6*
Dependency 41.6* 49.0*
All 43.5* (+15.7%) 53.4* (+17.9%)
*: the result is significantly better than the MLR with p < 0.05 in t-test

Experiments for Structure Learning
Relation Discovery Analysis
Discover inter-slot relations
connecting important slot pairs
The reference ontology with the most
frequent syntactic dependencies
locale_by_use
food expensiveness
seeking
relational_quantity
PREP_FOR
PREP_FOR
NN AMOD
AMOD
AMOD
desiring
DOBJ
type
food pricerange
DOBJ
AMOD AMOD
AMOD
task
area
PREP_IN
The automatically learned domain ontology aligns well with the reference one.
The data-driven one is more objective while expert-annotated one is more subjective.

Contributions of Semantic Decoding
Ontology
Induction
Structure
Learning
Semantic
Decoding
Intent
Prediction
SLU Modeling
 Ontology Induction and
Structure Learning enable
systems to automatically
acquire open domain
knowledge.
 MF-SLU for Semantic
Decoding is able to
1) unify the automatically
acquired knowledge
2) adapt to a domain-
specific setting
3) and then allows
systems to model
implicit semantics for
better understanding.

Low- and High-Level Understanding
Semantic concepts for individual utterances do not consider high-level
semantics (user intents)
The follow-up behaviors usually correspond to user intents
price=“cheap”
SLU Model
“can i have a cheap restaurant”
intent=navigation
restaurant=“legume”
time=“tonight”
SLU Model
“i plan to dine in legume tonight”
intent=reservation

Ontology
Induction
Structure
Learning
Semantic
Decoding
Intent
Prediction
SLU Modeling
SDS Flowchart – Intent Prediction

Outline
◦ What are they?
Semantic Decoding
Intent Prediction

[Chen & Rudnicky, SLT 2014; Chen et al., ICMI 2015]
Input: spoken utterances for
making requests about launching
an app
Output: the apps supporting the
required functionality
Intent Identification
◦ popular domains in Google Play
please dial a phone call to alex
Skype, Hangout, etc.
Intent Prediction of Mobile Apps [SLT’14c]
Chen and Rudnicky, "Dynamically Supporting Unexplored Domains in Conversational Interactions by Enriching Semantics with Neural Word Embeddings," in Proc. of SLT, 2014.

Input: single-turn request
Output: apps that are able to support the required functionality
Intent Prediction– Single-Turn Request
1
Enriched
Semantics
communication
.90
1
1
Utterance 1 i would like to contact alex
Word Observation Intended App
……
contact message Gmail Outlook Skypeemail
Test
.90
Reasoning with Feature-Enriched MF
Train
… your email, calendar, contacts…
… check and send emails, msgs …
Outlook
Gmail
IR for app
candidates
App Desc
Self-Train
Utterance
Test
Utterance
1
1
1
1
1
1
1
1 1
1
1 .90 .85 .97 .95
Feature
Enrichment
Utterance 1 i would like to contact alex
…
1
1
The feature-enriched MF-SLU unifies manually written knowledge and automatically
inferred semantics to predict high-level intents.

Intent Prediction– Multi-Turn Interaction [ICMI’15]
Input: multi-turn interaction
Output: apps the user plans to launch
Challenge: language ambiguity
1) User preference
2) App-level contexts
Chen et al., "Leveraging Behavioral Patterns of Mobile Applications for Personalized Spoken Language Understanding," in Proc. of ICMI, 2015. Data Available at http://AppDialogue.com/.
send to vivian
v.s.
Email? Message?
Communication
Idea: Behavioral patterns in history can help intent prediction.
previous
turn

Intent Prediction– Multi-Turn Interaction [ICMI’15]
Input: multi-turn interaction
Output: apps the user plans to launch
1
Lexical Intended App
photo check camera IMtell
take this photo
tell vivian this is me in the lab
CAMERA
IM
Train
Dialogue
check my grades on website
send an email to professor
…
CHROME
EMAIL
send
Behavior
History
null camera
.85
take a photo of this
send it to alice
CAMERA
IM
…
email
1
1
1 1
1
1 .70
chrome
1
1
1
1
1
1
chrome email
1
1
1
1
.95
.80 .55
User Utterance
Intended
App
Reasoning with Feature-Enriched MF
Test
Dialogue
take a photo of this
send it to alice
…
Chen et al., "Leveraging Behavioral Patterns of Mobile Applications for Personalized Spoken Language Understanding," in Proc. of ICMI, 2015. Data Available at http://AppDialogue.com/.
The feature-enriched MF-SLU leverages behavioral patterns to model contextual
information and user preference for better intent prediction.

Single-Turn Request: Mean Average Precision (MAP)
Multi-Turn Interaction: Mean Average Precision (MAP)
Feature Matrix
ASR Transcripts
LM MF-SLU LM MF-SLU
Word Observation 25.1 26.1
Feature Matrix
ASR Transcripts
MLR MF-SLU MLR MF-SLU
Word Observation 52.1 55.5
LM-Based IR Model (unsupervised)
Multinomial Logistic Regression
(supervised)
Experiments for Intent Prediction

Feature Matrix
ASR Transcripts
LM MF-SLU LM MF-SLU
Word Observation 25.1 29.2 (+16.2%) 26.1 30.4 (+16.4%)
Feature Matrix
ASR Transcripts
Word Observation 52.1 52.7 (+1.2%) 55.5 55.4 (-0.2%)
Modeling hidden semantics helps intent prediction especially for noisy data.

Feature Matrix
ASR Transcripts
LM MF-SLU LM MF-SLU
Word + Embedding-Based Semantics 32.0 33.3
Word + Type-Embedding-Based Semantics 31.5 32.9
Feature Matrix
ASR Transcripts
Word + Behavioral Patterns 53.9 56.6
Semantic enrichment provides rich cues to improve performance.

Feature Matrix
ASR Transcripts
LM MF-SLU LM MF-SLU
Word + Embedding-Based Semantics 32.0 34.2 (+6.8%) 33.3 33.3 (-0.2%)
Word + Type-Embedding-Based Semantics 31.5 32.2 (+2.1%) 32.9 34.0 (+3.4%)
Feature Matrix
ASR Transcripts
Word + Behavioral Patterns 53.9 55.7 (+3.3%) 56.6 57.7 (+1.9%)
Intent prediction can benefit from both hidden information and low-level semantics.

Ontology
Induction
Structure
Learning
Semantic
Decoding
Intent
Prediction
SLU Modeling
Contributions of Intent Prediction
 Feature-Enriched MF-SLU for
Intent Prediction is able to
1) unify the knowledge at
different levels
2) learn inference relations
between various
features
3) and create personalized
models by leveraging
contextual behaviors.

Reactive
Assistance
Proactive
Assistance
Inferences, User
Data
Back-end Data
Bases, Services and
Client Signals
User Experience
“call taxi”

Outline
◦ What are they?
Semantic Decoding
Intent Prediction

Conclusions
The work shows the feasibility and the potential for improving generalization,
maintenance, efficiency, and scalability of SDSs.
The proposed knowledge acquisition procedure enables systems to
automatically produce domain-specific ontologies.
The proposed MF-SLU unifies the automatically acquired knowledge, and
then allows systems to consider implicit semantics for better understanding.
◦ Better semantic representations for individual utterances
◦ Better high-level intent prediction about follow-up behaviors

Future Work
Apply the proposed technology to domain discovery
◦ not covered by the current systems but users are interested in
◦ guide the next developed domains
Improve the proposed approach by handling the uncertainty
SLU
SLU
Modeling
ASR
Knowledge
Acquisition
recognition
errors
unreliable
knowledge

d d d
U S1 S2
P(S1 | U) P(S2 | U)
…
Semantic Relation
Posterior Probability
Utterance
Slot Candidate
…
w1 w2 wd
Word Sequence: x
Word Vector: lw
Pooling Operation
R(U, S1) R(U, S2)
Knowledge Graph Propagation Matrix: Wp
Semantic Projection Matrix: Ws
Semantic Layer: y
Knowledge Graph Propagation Layer: lp
d
Sn
P(Sn | U)
Utterance Vector: lf
…
R(U, Sn)
Slot Vector: lf
Convolution Matrix: Wc
Convolutional Layer: lc
Towards Unsupervised Deep Learning
Treating MF as a one-layer neural
net, we can add more layers in
the model towards unsupervised
deep learning.

Take Home Message
Available big data w/o annotations
Challenge: how to acquire and
organize important knowledge, and
further utilize it for applications
Language understanding for AI
◦ language  action
◦ understand voice to control music, lights, etc.
◦ teach to let friends in by face recognition, etc.
Unsupervised or weakly-supervised
methods will be the future trend!
Deep language understanding is an
emerging field!

Q & A
THANKS FOR YOUR ATTENTIONS!!
• Chen et al., "Unsupervised Induction and Filling of Semantic Slots for Spoken Dialogue Systems Using Frame-Semantic Parsing," in Proc. of ASRU, 2013. (Best Student Paper
Award)
• Chen et al., "Jointly Modeling Inter-Slot Relations by Random Walk on Knowledge Graphs for Unsupervised Spoken Language Understanding," in Proc. of NAACL-HLT, 2015.
• Chen et al., "Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding," in Proc. of ACL-IJCNLP, 2015.
• Chen et al., “Dynamically Supporting Unexplored Domains in Conversational Interactions by Enriching Semantics with Neural Word Embeddings,” in Proc. of SLT, 2014.
• Chen et al., “Leveraging Behavioral Patterns of Mobile Applications for Personalized Spoken Language Understanding," in Proc. of ICMI, 2015.
• Chen et al., “Matrix Factorization with Domain Knowledge and Behavioral Patterns for Intent Modeling," in Extended Abstract of NIPS-SLU, 2015.
• Chen et al., “Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization," in Proc. of ICASSP, 2016.
77"SORRY, I DIDN'T GET THAT!" -- STATISTICAL LEARNING FROM DIALOGUES FOR INTELLIGENT ASSISTANTS

Statistical Learning from Dialogues for Intelligent Assistants

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