Learning Language from its
Perceptual Context
Ray Mooney
Department of Computer Sciences
University of Texas at Austin
Joint work with
David Chen
Rohit Kate
Yuk Wah Wong
1
Current State of
Natural Language Learning
• Most current state-of-the-art NLP systems
are constructed by training on large
supervised corpora.
–
–
–
–
Syntactic Parsing: Penn Treebank
Word Sense Disambiguation: SenseEval
Semantic Role Labeling: Propbank
Machine Translation: Hansards corpus
• Constructing such annotated corpora is
difficult, expensive, and time consuming.
2
Semantic Parsing
• A semantic parser maps a natural-language
sentence to a complete, detailed semantic
representation: logical form or meaning
representation (MR).
• For many applications, the desired output is
immediately executable by another program.
• Two application domains:
– GeoQuery: A Database Query Application
– CLang: RoboCup Coach Language
3
GeoQuery:
A Database Query Application
• Query application for U.S. geography database
[Zelle & Mooney, 1996]
How many states
User
does the
Mississippi run
through?
Semantic Parsing
10
Query answer(A, count(B,
(state(B),
C=riverid(mississippi),
traverse(C,B)),
DataBase
A))
4
CLang: RoboCup Coach Language
• In RoboCup Coach competition teams compete to
coach simulated soccer players
• The coaching instructions are given in a formal
language called CLang
Coach
If the ball is in our
penalty area, then all our
players except player 4
should stay in our half.
Simulated soccer field
Semantic Parsing
CLang
((bpos (penalty-area our))
(do (player-except our{4}) (pos (half our)))
5
Learning Semantic Parsers
• Manually programming robust semantic parsers
is difficult due to the complexity of the task.
• Semantic parsers can be learned automatically
from sentences paired with their logical form.
NLMR
Training Exs
Natural
Language
Semantic-Parser
Learner
Semantic
Parser
Meaning
Rep
6
Our Semantic-Parser Learners
• CHILL+WOLFIE (Zelle & Mooney, 1996; Thompson & Mooney,
1999, 2003)
– Separates parser-learning and semantic-lexicon learning.
– Learns a deterministic parser using ILP techniques.
• COCKTAIL (Tang & Mooney, 2001)
– Improved ILP algorithm for CHILL.
• SILT (Kate, Wong & Mooney, 2005)
– Learns symbolic transformation rules for mapping directly from NL to MR.
• SCISSOR (Ge & Mooney, 2005)
•
•
– Integrates semantic interpretation into Collins’ statistical syntactic parser.
WASP (Wong & Mooney, 2006; 2007)
– Uses syntax-based statistical machine translation methods.
KRISP (Kate & Mooney, 2006)
– Uses a series of SVM classifiers employing a string-kernel to iteratively build
semantic representations.
7
WASP
A Machine Translation Approach to Semantic Parsing
• Uses statistical machine translation
techniques
– Synchronous context-free grammars (SCFG)
(Wu, 1997; Melamed, 2004; Chiang, 2005)
– Word alignments (Brown et al., 1993; Och &
Ney, 2003)
• Hence the name: Word Alignment-based
Semantic Parsing
8
A Unifying Framework for
Parsing and Generation
Natural Languages
Machine
translation
9
A Unifying Framework for
Parsing and Generation
Natural Languages
Semantic parsing
Machine
translation
Formal Languages
10
A Unifying Framework for
Parsing and Generation
Natural Languages
Semantic parsing
Machine
translation
Tactical generation
Formal Languages
11
A Unifying Framework for
Parsing and Generation
Synchronous Parsing
Natural Languages
Semantic parsing
Machine
translation
Tactical generation
Formal Languages
12
A Unifying Framework for
Parsing and Generation
Synchronous Parsing
Natural Languages
Semantic parsing
Machine
translation
Compiling:
Aho & Ullman
(1972)
Tactical generation
Formal Languages
13
Synchronous Context-Free Grammars
(SCFG)
• Developed by Aho & Ullman (1972) as a
theory of compilers that combines syntax
analysis and code generation in a single
phase.
• Generates a pair of strings in a single
derivation.
14
Synchronous Context-Free Grammar
Production Rule
Natural language
Formal language
QUERY  What is CITY / answer(CITY)
15
Synchronous Context-Free Grammar
Derivation
QUERY
What
is
the
QUERY
answer
CITY
capital
of
(
capital
CITY
STATE
Ohio
CITY
(
loc_2
)
CITY
(
stateid
)
STATE
(
)
'ohio'
)
CITY
Ohio
the
capital
CITY
capital(CITY)
QUERY
CITY

 What
of
STATE
is
CITY
loc_2(STATE)
// answer(CITY)
answer(capital(loc_2(stateid('ohio'))))
STATE
Ohio
//stateid('ohio')
What is the capital
of
16
Probabilistic Parsing Model
d1
CITY
CITY
capital
capital
CITY
of
STATE
Ohio
(
loc_2
CITY
(
)
STATE
stateid
(
)
'ohio'
)
CITY  capital CITY / capital(CITY)
CITY  of STATE / loc_2(STATE)
STATE  Ohio / stateid('ohio')
17
Probabilistic Parsing Model
d2
CITY
CITY
capital
capital
CITY
of
RIVER
Ohio
(
loc_2
CITY
(
)
RIVER
riverid
(
)
'ohio'
)
CITY  capital CITY / capital(CITY)
CITY  of RIVER / loc_2(RIVER)
RIVER  Ohio / riverid('ohio')
18
Probabilistic Parsing Model
d1
d2
CITY
capital
(
loc_2
CITY
(
stateid
)
capital
STATE
(
CITY
)
'ohio'
loc_2
)
CITY  capital CITY / capital(CITY)
0.5
CITY  of STATE / loc_2(STATE)
0.3
STATE  Ohio / stateid('ohio')
0.5
+
(
CITY
(
riverid
λ
Pr(d1|capital of Ohio) = exp( 1.3 ) / Z
)
RIVER
(
)
'ohio'
)
CITY  capital CITY / capital(CITY)
0.5
CITY  of RIVER / loc_2(RIVER)
0.05
RIVER  Ohio / riverid('ohio')
0.5
+
λ
Pr(d2|capital of Ohio) = exp( 1.05 ) / Z
normalization constant
19
Overview of WASP
Unambiguous CFG of MRL
Lexical acquisition
Training set, {(e,f)}
Lexicon, L (an SCFG)
Parameter estimation
Training
SCFG parameterized by λ
Testing
Input sentence, e'
Semantic parsing
Output MR, f'
20
Tactical Generation
• Can be seen as inverse of semantic parsing
The goalie should always stay in our half
Semantic parsing
Tactical generation
((true) (do our {1} (pos (half our))))
21
Generation by Inverting WASP
• Same synchronous grammar is used for
both generation and semantic parsing.
Tactical generation:
Semantic
parsing:
NL:
Input
Output
MRL:
QUERY  What is CITY / answer(CITY)
22
Learning Language from
Perceptual Context
• Children do not learn language from annotated corpora.
• Neither do they learn language from just reading the
newspaper, surfing the web, or listening to the radio.
– Unsupervised language learning
– DARPA Learning by Reading Program
• The natural way to learn language is to perceive
language in the context of its use in the physical and
social world.
• This requires inferring the meaning of utterances from
their perceptual context.
23
Language Grounding
• The meanings of many words are grounded in our
perception of the physical world: red, ball, cup, run,
hit, fall, etc.
– Symbol Grounding: Harnad (1990)
• Even many abstract words and meanings are
metaphorical abstractions of terms grounded in the
physical world: up, down, over, in, etc.
– Lakoff and Johnson’s Metaphors We Live By
• Its difficult to put my words into ideas.
• Most NLP work represents meaning without any
connection to perception; circularly defining the
meanings of words in terms of other words or
meaningless symbols with no firm foundation.
24
“Mary is on the phone”
???
25
25
Ambiguous Supervision for
Learning Semantic Parsers
• A computer system simultaneously exposed to
perceptual contexts and natural language utterances
should be able to learn the underlying language
semantics.
• We consider ambiguous training data of sentences
associated with multiple potential MRs.
– Siskind (1996) uses this type “referentially uncertain”
training data to learn meanings of words.
• Extracting meaning representations from perceptual
data is a difficult unsolved problem.
– Our system directly works with symbolic MRs.
“Mary is on the phone”
???
27
27
???
“Mary is on the phone”
28
28
Ironing(Mommy, Shirt)
???
“Mary is on the phone”
29
29
Ironing(Mommy, Shirt)
Working(Sister, Computer)
???
“Mary is on the phone”
30
30
Ironing(Mommy, Shirt)
Carrying(Daddy, Bag)
Working(Sister, Computer)
???
“Mary is on the phone”
31
31
Ambiguous Training Example
Ironing(Mommy, Shirt)
Carrying(Daddy, Bag)
Working(Sister, Computer)
Talking(Mary, Phone)
Sitting(Mary, Chair)
???
“Mary is on the phone”
32
32
Next Ambiguous Training Example
Ironing(Mommy, Shirt)
Working(Sister, Computer)
Talking(Mary, Phone)
Sitting(Mary, Chair)
???
“Mommy is ironing a shirt”
33
33
Ambiguous Supervision for
Learning Semantic Parsers (cont.)
• Our model of ambiguous supervision
corresponds to the type of data that will be
gathered from a temporal sequence of
perceptual contexts with occasional
language commentary.
• We assume each sentence has exactly one
meaning in its perceptual context.
– Recently extended to handle sentences with no
meaning in its perceptual context.
• Each meaning is associated with at most
one sentence.
Sample Ambiguous Corpus
gave(daisy, clock, mouse)
Daisy gave the clock to the mouse.
ate(mouse, orange)
ate(dog, apple)
Mommy saw that Mary gave the
hammer to the dog.
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
The dog broke the box.
gave(woman, toy, mouse)
gave(john, bag, mouse)
John gave the bag to the mouse.
threw(dog, ball)
runs(dog)
The dog threw the ball.
saw(john, walks(man, dog))
Forms a bipartite graph
35
KRISPER:
KRISP with EM-like Retraining
• Extension of KRISP that learns from
ambiguous supervision.
• Uses an iterative EM-like self-training
method to gradually converge on a correct
meaning for each sentence.
KRISPER’s Training Algorithm
1. Assume every possible meaning for a sentence is correct
gave(daisy, clock, mouse)
Daisy gave the clock to the mouse.
ate(mouse, orange)
ate(dog, apple)
Mommy saw that Mary gave the
hammer to the dog.
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
The dog broke the box.
gave(woman, toy, mouse)
gave(john, bag, mouse)
John gave the bag to the mouse.
The dog threw the ball.
threw(dog, ball)
runs(dog)
saw(john, walks(man, dog))
37
KRISPER’s Training Algorithm
1. Assume every possible meaning for a sentence is correct
gave(daisy, clock, mouse)
Daisy gave the clock to the mouse.
ate(mouse, orange)
ate(dog, apple)
Mommy saw that Mary gave the
hammer to the dog.
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
The dog broke the box.
gave(woman, toy, mouse)
gave(john, bag, mouse)
John gave the bag to the mouse.
The dog threw the ball.
threw(dog, ball)
runs(dog)
saw(john, walks(man, dog))
38
KRISPER’s Training Algorithm
2. Resulting NL-MR pairs are weighted and given to KRISP
gave(daisy, clock, mouse)
1/2
Daisy gave the clock to the mouse.
1/2
1/4
1/4
Mommy saw that Mary gave the
1/4
hammer to the dog.
1/4
The dog broke the box.
1/5 1/5
1/5
1/5 1/5
1/3 1/3
John gave the bag to the mouse.
1/3
1/3
The dog threw the ball.
1/3
1/3
ate(mouse, orange)
ate(dog, apple)
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
gave(woman, toy, mouse)
gave(john, bag, mouse)
threw(dog, ball)
runs(dog)
saw(john, walks(man, dog))
39
KRISPER’s Training Algorithm
3. Estimate the confidence of each NL-MR pair using the
gave(daisy, clock, mouse)
resulting trained parser
0.92
Daisy gave the clock to the mouse.
0.11
0.32
0.88
Mommy saw that Mary gave the
0.22
hammer to the dog.
0.24
0.71 0.18
0.85
The dog broke the box.
0.14
0.95
0.24 0.89
John gave the bag to the mouse.
0.33
0.97
The dog threw the ball.
0.81
0.34
ate(mouse, orange)
ate(dog, apple)
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
gave(woman, toy, mouse)
gave(john, bag, mouse)
threw(dog, ball)
runs(dog)
saw(john, walks(man, dog))
40
KRISPER’s Training Algorithm
4. Use maximum weighted matching on a bipartite graph
to find the best NL-MR pairs [Munkres, 1957]
gave(daisy, clock, mouse)
0.92
Daisy gave the clock to the mouse.
0.11
0.32
0.88
Mommy saw that Mary gave the
0.22
hammer to the dog.
0.24
0.71 0.18
0.85
The dog broke the box.
0.14
0.95
0.24 0.89
John gave the bag to the mouse.
0.33
0.97
The dog threw the ball.
0.81
0.34
ate(mouse, orange)
ate(dog, apple)
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
gave(woman, toy, mouse)
gave(john, bag, mouse)
threw(dog, ball)
runs(dog)
saw(john, walks(man, dog))
41
KRISPER’s Training Algorithm
4. Use maximum weighted matching on a bipartite graph
to find the best NL-MR pairs [Munkres, 1957]
gave(daisy, clock, mouse)
0.92
Daisy gave the clock to the mouse.
0.11
0.32
0.88
Mommy saw that Mary gave the
0.22
hammer to the dog.
0.24
0.71 0.18
0.85
The dog broke the box.
0.14
0.95
0.24 0.89
John gave the bag to the mouse.
0.33
0.97
The dog threw the ball.
0.81
0.34
ate(mouse, orange)
ate(dog, apple)
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
gave(woman, toy, mouse)
gave(john, bag, mouse)
threw(dog, ball)
runs(dog)
saw(john, walks(man, dog))
42
KRISPER’s Training Algorithm
5. Give the best pairs to KRISP in the next iteration,
and repeat until convergence
gave(daisy, clock, mouse)
Daisy gave the clock to the mouse.
ate(mouse, orange)
ate(dog, apple)
Mommy saw that Mary gave the
hammer to the dog.
saw(mother,
gave(mary, dog, hammer))
broke(dog, box)
The dog broke the box.
gave(woman, toy, mouse)
gave(john, bag, mouse)
John gave the bag to the mouse.
The dog threw the ball.
threw(dog, ball)
runs(dog)
saw(john, walks(man, dog))
43
Results on Ambig-ChildWorld Corpus
100
90
Best F-measure
80
70
60
No ambiguity
Level 1 ambiguity
Level 2 ambiguity
Level 3 ambiguity
50
40
30
20
10
0
225
450
675
Number of training examples
900
New Challenge:
Learning to Be a Sportscaster
• Goal: Learn from realistic data of natural
language used in a representative context
while avoiding difficult issues in computer
perception (i.e. speech and vision).
• Solution: Learn from textually annotated
traces of activity in a simulated
environment.
• Example: Traces of games in the Robocup
simulator paired with textual sportscaster
commentary.
45
Grounded Language Learning
in Robocup
Robocup Simulator
Simulated
Perception
Perceived Facts
Sportscaster
Score!!!!
Grounded
Language Learner
Language
Generator
SCFG
Semantic
Parser
Score!!!!
46
Robocup Sportscaster Trace
Natural Language Commentary
Meaning Representation
badPass ( Purple1, Pink8 )
Purple goalie turns the ball over to Pink8
turnover ( Purple1, Pink8 )
kick ( Pink8)
pass ( Pink8, Pink11 )
Purple team is very sloppy today
kick ( Pink11 )
Pink8 passes the ball to Pink11
Pink11 looks around for a teammate
kick ( Pink11 )
ballstopped
kick ( Pink11 )
Pink11 makes a long pass to Pink8
pass ( Pink11, Pink8 )
kick ( Pink8 )
pass ( Pink8, Pink11 )
Pink8 passes back to Pink11
47
Robocup Sportscaster Trace
Natural Language Commentary
Meaning Representation
badPass ( Purple1, Pink8 )
Purple goalie turns the ball over to Pink8
turnover ( Purple1, Pink8 )
kick ( Pink8)
pass ( Pink8, Pink11 )
Purple team is very sloppy today
kick ( Pink11 )
Pink8 passes the ball to Pink11
Pink11 looks around for a teammate
kick ( Pink11 )
ballstopped
kick ( Pink11 )
Pink11 makes a long pass to Pink8
pass ( Pink11, Pink8 )
kick ( Pink8 )
pass ( Pink8, Pink11 )
Pink8 passes back to Pink11
48
Robocup Sportscaster Trace
Natural Language Commentary
Meaning Representation
badPass ( Purple1, Pink8 )
Purple goalie turns the ball over to Pink8
turnover ( Purple1, Pink8 )
kick ( Pink8)
pass ( Pink8, Pink11 )
Purple team is very sloppy today
kick ( Pink11 )
Pink8 passes the ball to Pink11
Pink11 looks around for a teammate
kick ( Pink11 )
ballstopped
kick ( Pink11 )
Pink11 makes a long pass to Pink8
pass ( Pink11, Pink8 )
kick ( Pink8 )
pass ( Pink8, Pink11 )
Pink8 passes back to Pink11
49
Robocup Sportscaster Trace
Natural Language Commentary
Meaning Representation
P6 ( C1, C19 )
Purple goalie turns the ball over to Pink8
P5 ( C1, C19 )
P1( C19 )
P2 ( C19, C22 )
Purple team is very sloppy today
P1 ( C22 )
Pink8 passes the ball to Pink11
Pink11 looks around for a teammate
P1 ( C22 )
P0
P1 ( C22 )
Pink11 makes a long pass to Pink8
P2 ( C22, C19 )
P1 ( C19 )
P2 ( C19, C22 )
Pink8 passes back to Pink11
50
Sportscasting Data
• Collected human textual commentary for the 4
Robocup championship games from 2001-2004.
– Avg # events/game = 2,613
– Avg # sentences/game = 509
• Each sentence matched to all events within
previous 5 seconds.
– Avg # MRs/sentence = 2.5 (min 1, max 12)
• Manually annotated with correct matchings of
sentences to MRs (for evaluation purposes only).
51
WASPER
• WASP with EM-like retraining to handle
ambiguous training data.
• Same augmentation as added to KRISP to
create KRISPER.
52
KRISPER-WASP
• First iteration of EM-like training produces very
noisy training data (> 50% errors).
• KRISP is better than WASP at handling noisy
training data.
– SVM prevents overfitting.
– String kernel allows partial matching.
• But KRISP does not support language generation.
• First train KRISPER just to determine the best
NL→MR matchings.
• Then train WASP on the resulting unambiguously
supervised data.
53
WASPER-GEN
• In KRISPER and WASPER, the correct MR for
each sentence is chosen based on maximizing the
confidence of semantic parsing (NL→MR).
• Instead, WASPER-GEN determines the best
matching based on generation (MR→NL).
• Score each potential NL/MR pair by using the
currently trained WASP-1 generator.
• Compute NIST MT score (alternative to BLEU
score) between the generated sentence and the
potential matching sentence.
54
Strategic Generation
• Generation requires not only knowing how
to say something (tactical generation) but
also what to say (strategic generation).
• For automated sportscasting, one must be
able to effectively choose which events to
describe.
55
Example of Strategic Generation
pass ( purple7 , purple6 )
ballstopped
kick ( purple6 )
pass ( purple6 , purple2 )
ballstopped
kick ( purple2 )
pass ( purple2 , purple3 )
kick ( purple3 )
badPass ( purple3 , pink9 )
turnover ( purple3 , pink9 )
56
Example of Strategic Generation
pass ( purple7 , purple6 )
ballstopped
kick ( purple6 )
pass ( purple6 , purple2 )
ballstopped
kick ( purple2 )
pass ( purple2 , purple3 )
kick ( purple3 )
badPass ( purple3 , pink9 )
turnover ( purple3 , pink9 )
57
Learning for Strategic Generation
• For each event type (e.g. pass, kick)
estimate the probability that it is described
by the sportscaster.
• Requires NL/MR matching that indicates
which events were described, but this is not
provided in the ambiguous training data.
– Use estimated matching computed by
KRISPER, WASPER or WASPER-GEN.
– Use a version of EM to determine the
probability of mentioning each event type just
based on strategic info.
58
Iterative Generation Strategy Learning
(IGSL)
• Directly estimates the likelihood of
commenting on each event type from the
ambiguous training data.
• Uses self-training iterations to improve
estimates (à la EM).
• Uses events not associated with any NL as
negative evidence for commenting on that
event type.
Demo
• Game clip commentated using WASPERGEN with EM-based strategic generation,
since this gave the best results for generation.
• FreeTTS was used to synthesize speech from
textual output.
Experimental Evaluation
• Generated learning curves by training on all
combinations of 1 to 3 games and testing on all
games not used for training.
• Baselines:
– Random Matching: WASP trained on random choice of
possible MR for each comment.
– Gold Matching: WASP trained on correct matching of MR
for each comment.
• Metrics:
– Precision: % of system’s annotations that are correct
– Recall: % of gold-standard annotations correctly produced
– F-measure: Harmonic mean of precision and recall
Evaluating Matching Accuracy
• Measure how accurately various methods
assign MRs to sentences in the ambiguous
training data.
• Use gold-standard matches to evaluate
correctness.
Results on Matching
0.8
0.7
F-measure
0.6
random
KRISPER
WASPER
WASPER-GEN
0.5
0.4
0.3
0.2
0.1
0
Average results on leave-one-gameout cross-validation
Evaluating Semantic Parsing
• Measure how accurately learned parser
maps sentences to their correct meanings in
the test games.
• Use the gold-standard matches to determine
the correct MR for each sentence that has
one.
• Generated MR must exactly match goldstandard to count as correct.
Results on Semantic Parsing
Evaluating Tactical Generation
• Measure how accurately NL generator
produces English sentences for chosen MRs
in the test games.
• Use gold-standard matches to determine the
correct sentence for each MR that has one.
• Use NIST score to compare generated
sentence to the one in the gold-standard.
Results on Tactical Generation
Evaluating Strategic Generation
• In the test games, measure how accurately
the system determines which perceived
events to comment on.
• Compare the subset of events chosen by the
system to the subset chosen by the human
annotator (as given by the gold-standard
matching).
Results on Strategic Generation
0.8
inferred from
WASP
inferred from
KRISPER
inferred from
WASPER
inferred from
WASPER-GEN
IGSL
0.7
F-measure
0.6
0.5
0.4
0.3
0.2
0.1
0
Average results on leave-onegame-out cross-validation
inferred from
gold matching
Human Evaluation
(Quasi Turing Test)
• Asked 4 fluent English speakers to evaluate overall
quality of sportscasts.
• Randomly picked a 2 minute segment from each of the
4 games.
• Each human judge evaluated 8 commented game clips,
each of the 4 segments commented once by a human
and once by the machine when tested on that game.
• The 8 clips presented to each judge were shown in
random counter-balanced order.
• Judges were not told which ones were human or
machine generated.
70
Human Evaluation Metrics
Score
5
4
3
2
1
English
Fluency
Flawless
Good
Semantic
Correctness
Always
Usually
Sportscasting
Ability
Excellent
Good
Non-native
Disfluent
Gibberish
Sometimes
Rarely
Never
Average
Bad
Terrible
71
Results on Human Evaluation
English
Commentator Fluency
Human
3.94
Machine
3.44
Difference
0.5
Semantic
Correctness
4.25
3.56
Sportscasting
Ability
3.63
2.94
0.69
0.69
72
Immediate Future Directions
• Use strategic generation information to
improve resolution of ambiguous training
data.
• Improve WASP’s ability to handle noisy
training data.
• Improve simulated perception to extract
more detailed and interesting symbolic facts
from the simulator.
Machine Learning Research Direction
• Learning from ambiguous/weak supervision (Siskind,1996).
• Multiple Instance Learning assumes weak supervision for
classification in the form of positive “bags” which contain
at least on positive instance (Dietterich, et al., 1997)
• Learning with Structured Data assumes I/O are complex
data structures (e.g. strings or graphs) rather than simple
vectors and class labels (Bakir et al., 2007)
• Need, Structured Multiple Instance Learning where an
input string is paired with a set of possible MRs, one of
which is likely to be correct.
Longer Term Future Directions
• Apply approach to learning situated
language in a computer video-game
environment (Gorniak & Roy, 2005)
– Teach game AI’s how to talk to you!
• Apply approach to captioned images or
video using computer vision to extract
objects, relations, and events from real
perceptual data (Fleischman & Roy, 2007)
Blatant Talk Advertisement
• Watch, Listen & Learn: Co-training on
Captioned Images and Videos, S. Gupta,
J. Kim, K. Grauman, and R. Mooney
• Semi-Supervised Learning session,
Thursday, 11:40, R002
Conclusions
• Current language learning work uses expensive,
unrealistic training data.
• We have developed language learning systems
that can learn from sentences paired with an
ambiguous perceptual environment.
• We have evaluated it on learning to sportscast
simulated Robocup games where it learns to
commentate games almost as well as humans.
• Learning to connect language and perception is
an important and exciting research problem.
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