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Overcoming the Lack of Parallel Data in Machine...
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Overcoming the Lack of Parallel Data in Machine Translation
Kevin Knight & David Chiang USC/ISI
MURI Review
November 14, 2014
Two Talks: • Exploiting monolingual data • Exploiting deeper representations
Talk #1: Exploiting Monolingual Data cross-site collaboration (ISI/UT/CMU/MIT): using dependency parsers and word aligners to extract translation patterns from non-parallel text
Exploiting Monolingual Data
Malagasy text
Deciphering Engine
Malagasy/English translation dictionary, models for use in MT
Decipherment into English
treat Malagasy as a code for English... and decode
Step by Step
Letter Substitution Ciphers [Ravi/Knight 08, Ravi/Knight 09a]
Phoneme Substitution [Ravi/Knight 09b]
Word Substitution Ciphers [Ravi/Knight 11a, Dou/Knight 12]
Foreign Language as a Cipher for English [Ravi/Knight 11a, Dou/Knight 12, Dou/Knight 13, Dou/Vaswani/Knight 14]
Historical Ciphers [Snyder/Barzilay/Knight 10, Knight/Megyesi/Schaefer 11, Ravi/Knight 11b, Reddy/Knight 11]
2009 2010 2011 2012 2013 2008 2014
Letter Substitution Cipher
Letter Frequencies
2-grams: 3-grams: ? - 99 ? - ^ 47 C : 66 C : G 23 - ^ 49 Y ? - 22 : G 48 y ? - 18 z ) 44 H C | 17
Tendencies:
A, E, I, O, U followed by 3 and j A, E, I, O, U preceded by z and >
0
50
100
150
200
250
300
350
400
450
^ | z G- C Z j ! 3 Y ) U y + O F H = : I > b g RM E X c ? 6 K N n < / Q ~ A D p B P " S l Lkm1 & e 5f v h rJ 7 i T s o ] a t d u89[ 0w_ W 4 q @x2#, ` \*%
Letter Distributions
?]8R j 3 |^+C~DgBF/[4TM15-: 7Q >z6X9s qxJmvknwtrfhoai Lc bp uKei W”=Gd&<)OAZUEI y!Y PHN
unaccented Roman letters
circumflexed vowels
underlined letters
letters grouped if they have similar contexts (L/R neighbors)
thanks Jon Graehl
Statistical Modeling
P(c | p) P(p)
plaintext p ciphertext c
“key”
Statistical Modeling
ciphertext c P(c | p) P(p)
plaintext p
Find substitution-table values that maximize P(c) = Σp P(p, c) = Σp P(p) P(c | p)
best guess plaintext p
Find plaintext p that maximizes P(p | c) ∼ P(p) P(c | p)
EM
Viterbi
LM
plaintext samples, unrelated to ciphertext
ciphertext c
“key”
Letter Substitution: Results
[Ravi & Knight 08]
Plus other methods, such as based on integer linear programming.
Word Substitution
Each code number represents a plaintext word, not letter
Encipherment Key
Decipherment Key
Word Substitution Keys
Word Substitution
[Dou & Knight 2012]
Foreign Language as a Code for English
!l@!m !lywm !lth!ny& !l@!m !lm!Dy Sfr @!m th!ny& @!m 1992 @!m 1993 ywm !l!sbw@ !lm!Dy fy !ldqyq& !lsn& !lj!ry& !lsn& !lsh=hr !lm!Dy !lsh=hr !lj!ry snw!t sn& =hdh! !l@!m s!@& !l@Sr @!m 1991
!l@Swr =hdh! !lsh=hr fy ywm nys!n !sbw@ =hdh=h !l!'y!m qbl !'y!m fy !l@Sr mn !lsn& !lsnw!t b@d ywm !l!y!m 13 nys!n 1994 !lth!ny& @shr& thl!th& !y!m qbl !sbw@yn fy !lywm !lt!ly sh@b!n tmwz 3 dhw !lHj& 1414 fy shb!T !lm!Dy qbl ywmyn
@!m 1990 w!lth!ny& fy !lywm mn !lsh=hr !lj!ry !lqrn !'y!m @!m!aN !ls!@& 17 shb!T 1994 thl!th snw!t dqyq& =hdh=h !lsn& ywmyn mn !l@!m !lm!Dy !lsn& !lmqbl& fy !lsn& kl ywm fy !l@!m !lm!Dy
13 4 Hzyr!n 1967 12 fy 12 Hzyr!n 1993 7 5 Hzyr!n 1967 6 fy 30 Hzyr!n 1989 6 30 Hzyr!n 1989 4 fy 30 Hzyr!n 1994 4 fy 30 Hzyr!n 1993 3 fy 19 Hzyr!n 1967 2 ywm 30 Hzyr!n 1989 2 w 6 Hzyr!n 1994 2 qbl 5 Hzyr!n 1967 2 fy 9 Hzyr!n 1967 2 fy 7 Hzyr!n 1981 2 fy 6 Hzyr!n 1994 2 fy 5 Hzyr!n 1967
2 fy 30 Hzyr!n 1995 2 fy 18 Hzyr!n 1994 2 fy 14 Hzyr!n 1993 2 fy 14 Hzyr!n 1991 2 fy 12 Hzyr!n 1990 2 7 Hzyr!n 1994 2 6 Hzyr!n 1941 2 26 Hzyr!n 1994 2 21 Hzyr!n 1994 2 1 Hzyr!n 1994 2 19 Hzyr!n 1965 2 18 Hzyr!n 1994 2 18 Hzyr!n 1940 2 12 Hzyr!n 1993 2 11 Hzyr!n 1994
<n> Hzyr!n <n>
Foreign Language as a Code for English
Foreign Language as a Code for English
13 4 Hzyr!n 1967 12 fy 12 Hzyr!n 1993 7 5 Hzyr!n 1967 6 fy 30 Hzyr!n 1989 6 30 Hzyr!n 1989 4 fy 30 Hzyr!n 1994 4 fy 30 Hzyr!n 1993 3 fy 19 Hzyr!n 1967 2 ywm 30 Hzyr!n 1989 2 w 6 Hzyr!n 1994 2 qbl 5 Hzyr!n 1967 2 fy 9 Hzyr!n 1967 2 fy 7 Hzyr!n 1981 2 fy 6 Hzyr!n 1994 2 fy 5 Hzyr!n 1967
<n> Hzyr!n <n>
Search query Documents January 4, 1967 8040 February 4, 1967 9270 March 4, 1967 10700 April 4, 1967 21800 May 4, 1967 14000 June 4, 1967 39300 July 4, 1967 12600 August 4, 1967 7970 September 4, 1967 7390 October 4, 1967 8800 November 4, 1967 6560 December 4, 1967 9770
Parsing Helps Decipherment
How much foreign text (running words)
Accuracy, learned bilingual dictionary
Decipherment with parsing (Dou/Knight 2013)
Spanish/English
* of most freq 5000 word types, 1-best translation in parallel dict
*
Decipherment without parsing (Dou/Knight 2012)
Adjacent bigrams Naciones Unidas dogs run perros corren blue rock no necessito United Nations piedra azul need not
Dependency bigrams Naciones Unidas run dogs corren perros rock blue necessito no Nations United piedra azul need not
?
Exploit parsers in both languages
Accurate Parsing Is Important
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
100k 1m 10m
Accu
racy
Num of Tokens
Adjacent
Dep1
Dep2
Dep3
Malagasy/English
Malagasy parser #1
Malagasy parser #2
Malagasy parser #3
** of most freq 5000 word types, any of 5-best in parallel dict
**
Increased parsing data by manual projection through parallel data using online dictionary Improve POS tags with parallel data. UT tagger + CMU Turboparser. CMU Turbotagger and Turboparser
Malagasy English maro many monisipaly municipal ratsy bad midadasika large vavy female lalina fundamental manokana special taitra surprised
Combining Parallel and Non-Parallel Data
small word-aligned parallel corpus
decipherment of large monolingual corpus
improve decipherment by seeding with dictionary derived from parallel data
improved machine translation
Combining Parallel and Non-Parallel Data
small word-aligned parallel corpus
decipherment of large monolingual corpus
improve decipherment by seeding with dictionary derived from parallel data
improve alignment of parallel data using large monolingual resources
improved machine translation
Combining Parallel and Non-Parallel Data
small word-aligned parallel corpus
decipherment of large monolingual corpus
improve decipherment by seeding with dictionary derived from parallel data
improve alignment of parallel data using large monolingual resources
improved machine translation
Joint objective function: Πe,f P(f | e) α · Πf Σe P(e) P(f | e)
parallel data
non-parallel data
find bilingual dictionary that makes both model components happy
Combining Parallel and Non-Parallel Data
Small bilingual Malagasy/English text (need to align words [Brown et al 93])
Large Malagasy monolingual text (need to decipher [Dou & Knight 13])
Decipherment helps Word Alignment
Decipherment helps Machine Translation
joint
Bleu
Decipherment: Next Steps
Integrate decipherment and foreign language parsing – “project English syntax through non-parallel data” – learn better Malagasy parser automatically
Fully integrated processing: – decipherment + alignment + parsing
More accurate decipherment – word classes/embeddings
Open-source tool for decipherment
Need to keep going!
Work by Others on Decipherment • "Simple Effective Decipherment via Combinatorial Optimization," (T. Berg-Kirkpatrick and D.
Klein), Proc. EMNLP, 2011. • "Deciphering Foreign Language by Combining Language Models and Context Vectors," (M.
Nuhn, A. Mauser, and H. Ney), Proc. ACL, 2012. • "Decipherment Complexity in 1:1 Substitution Ciphers," (M. Nuhn and H. Ney), Proc. ACL,
2013. • "Beam Search for Solving Substitution Ciphers," (M. Nuhn, J. Schamper, and H. Ney), Proc. ACL,
2013. • “Unsupervised Consonant-Vowel Prediction over Hundreds of Languages,” (Y. Kim and B.
Snyder), Proc. ACL, 2013. • “EM Decipherment for Large Vocabularies,” (M. Nuhn and H. Ney), Proc. ACL, 2014. • “Scalable Decipherment for Machine Translation via Hash Sampling,” (S. Ravi), Proc. ACL, 2013. • “Decipherment with a Million Random Restarts,” (T. Berg-Kirkpatrick and D. Klein), Proc.
EMNLP, 2013. • “Combining Bilingual and Comparable Corpora for Low Resource Machine Translation,” (A.
Irvine and C. Callison-Burch), Proc. WMT, 2013. • “Hallucinating Phrase Translations for Low Resource MT” (A. Irvine and C. Callison-Burch), Proc.
CoNLL, 2014. • “Solving Substitution Ciphers with Combined Language Models” (B. Hauer, R. Hayward, and G.
Kondrak), Proc. COLING, 2014.
Talk #2: Exploiting Deeper Representations cross-site collaboration (ISI/CMU): mapping language onto meaning mapping meaning onto language combining these for meaning-based MT
Why Meaning-Based MT?
• That’s what translation is: – build grammatical target text… – that preserves the meaning of the source
Oh, we got the meaning wrong…
We got the right meaning, but rendered it disfluently…
- or -
Meaning-Based MT • What content goes into the meaning
representation? Abstract Meaning Representation (AMR)
• How are meaning representations probabilistically generated, transformed, scored, ranked? How to represent knowledge that drives these processes? Automata theory, efficient algorithms
• How can a full MT system be built? Engineering, modeling, features, training
MURI
Machine Translation Automata
Phrase-based MT
Syntax-based MT
source string
target string
source string
source tree
target tree
target string
Finite-State Transducer (FST)
k
n
i
g
h
t
q k q2 *e*
q2 n q N
q i q AY q g q3 *e*
q4 t qfinal T q3 h q4 *e*
Original input: Transformation: q k
n
i
g
h
t
FST
q q2
qfinal q3 q4
k : *e*
n : N
h : *e*
g : *e* t : T
i : AY
Finite-State (String) Transducer
q2 n
i
g
h
t
q k q2 *e*
q2 n q N
q i q AY q g q3 *e*
q4 t qfinal T q3 h q4 *e*
Original input: Transformation: k
n
i
g
h
t
FST
q q2
qfinal q3 q4
k : *e*
n : N
h : *e*
g : *e* t : T
i : AY
Finite-State (String) Transducer
N
q i
g
h
t
q k q2 *e*
q2 n q N
q i q AY q g q3 *e*
q4 t qfinal T q3 h q4 *e*
Original input: Transformation: k
n
i
g
h
t
FST
q q2
qfinal q3 q4
k : *e*
n : N
h : *e*
g : *e* t : T
i : AY
Finite-State (String) Transducer
q g
h
t
q k q2 *e*
q2 n q N
q i q AY q g q3 *e*
q4 t qfinal T q3 h q4 *e*
AY
N
Original input: Transformation: k
n
i
g
h
t
FST
q q2
qfinal q3 q4
k : *e*
n : N
h : *e*
g : *e* t : T
i : AY
Finite-State (String) Transducer
q3 h
t
q k q2 *e*
q2 n q N
q i q AY q g q3 *e*
q4 t qfinal T q3 h q4 *e*
AY
N
Original input: Transformation: k
n
i
g
h
t
FST
q q2
qfinal q3 q4
k : *e*
n : N
h : *e*
g : *e* t : T
i : AY
Finite-State (String) Transducer
q4 t
q k q2 *e*
q2 n q N
q i q AY q g q3 *e*
q4 t qfinal T q3 h q4 *e*
AY
N
Original input: Transformation: k
n
i
g
h
t
FST
q q2
qfinal q3 q4
k : *e*
n : N
h : *e*
g : *e* t : T
i : AY
Finite-State (String) Transducer
q k q2 *e*
q2 n q N
q i q AY q g q3 *e*
q4 t qfinal T q3 h q4 *e*
T
qfinal
AY
N
k
n
i
g
h
t
Original input: Transformation:
FST
q q2
qfinal q3 q4
k : *e*
n : N
h : *e*
g : *e* t : T
i : AY
S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
Original input: Transformation:
q S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
Top-Down Tree Transducer (W. Rounds 1970; J. Thatcher 1970)
S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
Original input: Transformation:
q S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
Top-Down Tree Transducer (W. Rounds 1970; J. Thatcher 1970)
q S
x0:NP VP
s x0, wa, r x2, ga, q x1
x1:VBZ x2:NP
0.2
S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
Original input: Transformation:
s NP
PRO
he
q VBZ
enjoys
r NP
VBG
listening
VP
P
to
NP
SBAR
music
, ,
Top-Down Tree Transducer (W. Rounds 1970; J. Thatcher 1970)
, wa , ga
S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
Original input: Transformation:
s NP
PRO
he
q VBZ
enjoys
r NP
VBG
listening
VP
P
to
NP
SBAR
music
, ,
Top-Down Tree Transducer (W. Rounds 1970; J. Thatcher 1970)
, wa , ga
s NP
PRO
kare
he
0.7
S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
Original input: Transformation:
q VBZ
enjoys
r NP
VBG
listening
VP
P
to
NP
SBAR
music
, kare wa ,
Top-Down Tree Transducer (W. Rounds 1970; J. Thatcher 1970)
, , ga
S
NP VP
PRO
he
VBZ
enjoys
NP
VBG
listening
VP
P
to
NP
SBAR
music
kare kiku ongaku o wa daisuki desu ga no
Original input: Final output:
, , , , , , , ,
Top-Down Tree Transducer (W. Rounds 1970; J. Thatcher 1970)
General-Purpose Algorithms for Tree Automata String Automata
Algorithms Tree Automata
Algorithms N-best … … paths through an WFSA
(Viterbi, 1967; Eppstein, 1998) … trees in a weighted forest (Jiménez & Marzal, 2000; Huang & Chiang, 2005)
EM training Forward-backward EM (Baum/Welch, 1971; Eisner 2003)
Tree transducer EM training (Graehl & Knight, 2004)
Determinization … … of weighted string acceptors (Mohri, 1997)
… of weighted tree acceptors (Borchardt & Vogler, 2003; May & Knight, 2005)
Intersection WFSA intersection Tree acceptor intersection
Applying transducers string WFST WFSA tree TT weighted tree acceptor
Transducer composition WFST composition (Pereira & Riley, 1996)
Many tree transducers not closed under composition (Maletti et al 09)
General-purpose tools Carmel, OpenFST Tiburon (May & Knight 10)
Machine Translation
Phrase-based MT
Syntax-based MT
Meaning-based MT source string
meaning graphs
target string
source string
target string
source string
source tree
target tree
target string
source tree
target tree
Semantic Graphs “Pascale was charged with public intoxication and resisting arrest.”
15,000 sentences have been annotated with Abstract Meaning Representation (AMR) in [Banarescu et al 13].
Abstract Meaning Representation (AMR) Pascale was charged with public intoxication and resisting arrest. (c / charge-05 :ARG1 (p / person :name (n / name :op1 “Pascale”)) :ARG2 (a / and :op1 (i / intoxicate-01 :ARG1 p :location (p2 / public)) :op2 (r / resist-01 :ARG0 p :ARG1 (a / arrest-01 :ARG1 p))))
PropBank frames
Named entities of 80 types
Entities play multiple roles (coreference)
100 semantic roles
Implicit roles
Modality
Negation
Questions
Full exploitation of predicates
Bond investors might not react. (p / possible :domain (r / react-01 :polarity – :arg0 (p2 / person :arg0-of (i / invest-01 :arg1 (b / bond)))
Abstraction from POS Light Verbs Cause Sub-events etc.
Graph Automata for NLU and NLG String Automata
Algorithms Tree Automata
Algorithms Graph Automata
Algorithms N-best answer extraction
… paths through an WFSA (Viterbi, 1967; Eppstein, 1998)
… trees in a weighted forest (Jiménez & Marzal, 2000; Huang & Chiang, 2005)
Investigating: • Linguistically adequate representations • Efficient algorithms Using them in: • Text Meaning (NLU) • Meaning Text (NLG) • Meaning-based MT
Unsupervised EM training
Forward-backward EM (Baum/Welch, 1971; Eisner 2003)
Tree transducer EM training (Graehl & Knight, 2004)
Determinization, minimization
… of weighted string acceptors (Mohri, 1997)
… of weighted tree acceptors (Borchardt & Vogler, 2003; May & Knight, 2005)
Intersection WFSA intersection Tree acceptor intersection
Application of transducers
string WFST WFSA tree TT weighted tree acceptor
Composition of transducers
WFST composition (Pereira & Riley, 1996)
Many tree transducers not closed under composition (Maletti et al 09)
Software tools Carmel, OpenFST Tiburon (May & Knight 10) ISI jointly with CMU & ND
Mapping Between Meaning and Text
the boy wants to see WANT
BOY
SEE
instance
instance
instance agent
patient
agent
Umuhungu arashaka kubona.
Mapping Between Meaning and Text
the boy wants to be seen WANT
BOY
SEE
instance
instance
instance agent
patient
patient
Umuhungu arashaka kubonwa.
Mapping Between Meaning and Text
the boy wants to see the girl WANT
BOY
SEE
instance
instance
instance agent
patient
patient
GIRL
instance
agent
Umuhungu arashaka kubona umukobwa
Mapping Between Meaning and Text
the boy wants to see himself
WANT
BOY
SEE
instance
instance
instance agent
patient
patient agent
Umuhungu arashaka kwibona.
DAG-to-Tree Transducer [Kamimura & Slutski 82; Quernheim & Knight 2012ab]
• Bottom-up transformation of graph to tree
Hyperedge Replacement Grammar [Drewes et al 97; Chiang et al 2013]
probabilistic rules
initial graph
final graph
HRG Derivation
instance ARG0
WANT
B
ARG1
instance
ARG0
BELIEVE
ARG1
G
instance
WANT
ARG1
= boy wants girl to believe that he is wanted
LET’S DERIVE THIS:
HRG Derivation
instance ARG0
WANT
B
ARG1
instance
ARG0
BELIEVE
ARG1
G
instance
WANT
ARG1
instance
ARG0
WANT
B
X
“the boy wants something involving himself”
ARG1
LET’S DERIVE THIS:
HRG Derivation
instance ARG0
WANT
B
ARG1
instance
ARG0
BELIEVE
ARG1
G
instance
WANT
ARG1
“the boy wants something involving himself”
instance
ARG0
WANT
B
X
ARG1
LET’S DERIVE THIS:
HRG Derivation
instance ARG0
WANT
B
ARG1
instance
ARG0
BELIEVE
ARG1
G
instance
WANT
ARG1
instance ARG0
WANT
B
X instance
ARG0
BELIEVE
G
“the boy wants the girl to believe something involving him”
ARG1
LET’S DERIVE THIS:
HRG Derivation
instance ARG0
WANT
B
ARG1
instance
ARG0
BELIEVE
ARG1
G
instance
WANT
ARG1
instance ARG0
WANT
B
X instance
ARG0
BELIEVE
G
“something involving B”
ARG1
LET’S DERIVE THIS:
HRG Derivation
instance ARG0
WANT
B
ARG1
instance
ARG0
BELIEVE
ARG1
G
instance
WANT
ARG1
instance ARG0
WANT
B
instance
ARG0
BELIEVE
G
instance
WANT
ARG1
ARG1
ARG1
FINISHED!
LET’S DERIVE THIS:
Synchronous Hyperedge Replacement Grammar (SHRG)
[Chiang et al 13]
• Each SHRG rule outputs a graph fragment and a tree fragment simultaneously
• Used for transducing meaning to language, and vice-versa
SHRG Derivation
S
wants B INF
instance ARG0
WANT
B
X
“the boy wants something involving himself”
SHRG Derivation
S
wants B INF
to believe G S
instance ARG0
WANT
B
X
ARG1
instance
ARG0
BELIEVE
G
“something involving B”
SHRG Derivation
instance ARG0
WANT
B
ARG1
instance
ARG0
BELIEVE
G
instance
WANT
ARG1
S
wants B INF
to believe G S
is wanted he
FINISHED!
Formal Properties of HRG Acceptors
novel algorithm
very pleasant!
mildly unpleasant
d=input graph outdegree T=treewidth complexity
Formal Properties of SHRG transducers
New device
New Work
• DAG-to-tree transducer formalism – Prague JHU workshop, summer 2014
• David Chiang, Dan Gildea (US) • Frank Drewes, Giorgio Satta (Europe)
• Linguistic suitability of formalisms – Explain meaning/string corpora
Strings Graphs
FSA CFG DAG acceptor HRG
probabilistic yes yes yes yes
intersects with finite-
state yes yes yes yes
EM training yes yes yes yes
transduction O(n) O(n3) O(|Q|T+1n) O((3dn)T+1)
implemented yes yes yes yes
Results for DAG automata
d = graph degree for AMR, high in practice T = treewidth complexity for AMR, low in practice (2-3)
efficient algorithms for k-best, EM, etc + tools + high impact on practical machine translation
invented tree transducers
worked out basic theoretical properties of tree transducers
over 30 years
invented graph grammars & basic recognition invented synchronous graph grammars
for transduction. also: probabilities, training algorithms, theorems, toolkits.
improved algorithms (Prague 2014) summer workshop
aiming at high impact on MT
Linguistic Suitability of Formalisms
• Concisely capture all of the graph/string pairs in a corpus
• By manually building linguistic mapping knowledge
then, measure coverage & conciseness
“Stress Test” Data
• 10,000 smallest semantic graphs composed of: – Predicates BELIEVE and WANT – Entities BOY and GIRL
• Plus 10 English string realizations of each graph
He wants her to believe he wants her.
Solutions We Designed and Tested
All transducer cascades are bidirectional: we run forwards for NL generation task, and backwards for NL understanding task.
graph SHRG
string tree graph
string
graph DAG2Tree string tree xLNTs (take yield)
tree DAG2Tree (tree-ify)
xLNT (introduce
verbs)
xLNTs (take yield) tree
xLNT (introduce pronouns)
Empirical Results
Data is available -- amr.isi.edu/download/boygirl.tgz We hope that others will continue to design more elegant, efficient formalisms to capture the meaning/text relation!
Novel Contributions
• Hyperedge Replacement Grammar (HRG) – synchronous version for transduction [ACL 2013] – proof-of-concept MT system [COLING 2012]
• Novel algorithm for graph parsing [ACL 2013]
• Empirical fitness to linguistic data [LREC 2014]
• Map of theoretical and computational properties – closure, complexity [FSMNLP 2015, subm.]
Bolinas Graph Processing Toolkit
Graph Formalisms: Next Steps
• Graph transduction workshop – One week in Dagstuhl, Germany (March 2015) – 35 attendees from Theory and NLP – Organizers: F. Drewes, K. Knight, M. Kuhlman
• Automatic extraction/use of graph grammars
– Hook up with manually-created AMR bank of 15,000 sentences (fiction, news, blog)
• AMR to English generation • English to AMR parsing
end