Introduction to the Language Technologies Institute
Fall, 2008Jaime [email protected]
School of Computer Science at Carnegie Mellon University
Computer Science Department (theory, systems)
Robotics Institute (space, industry, medical)
Language Technologies Institute (MT, speech, IR)
Human-Computer Interaction Inst. (Ergonomics)
Institute for Software Research Int. (SE)
Machine Learning Department (ML theory)
Entertainment Technologies (Animation, graphics)
Language Technologies Institute Founded in 1986 as the Center for
Machine Translation (CMT). Became Language Technologies
Institute in 1996, unifying CMT, Comp Ling program.
Current Size: 197 FTEs 27 Faculty (including joint appointments) 25 Staff 125 Graduate Students (90 PhD, 40 MLT) 10 Visiting Scholars
LTI Bill of Rights Get the rightright information To the right people At the right time On the right medium In the right language At the right level of detail
Slogan Challenges …right
information …right people …right time …right medium …right language …right detail
IR, filtering, TC, … routing,
personalization, … anticipatory analysis, … text, speech, video, … translation, bio, … summarization,
expansion
“…on the Right Medium” Speech Recognition
SPHINX (Reddy, Rudnicky Rosenfeld, …) JANUS (Waibel, Schultz, …)
Speech Synthesis Festival (Black, Lenzo)
Handwriting & Gesture Recognition ISL (Waibel, J. Yang)
Multimedia Integration (CSD) Informedia (Wactlar, Hauptmann, …)
“… in the Right Language” High-Accuracy Interlingual MT
KANT (Nyberg, Mitamura) Parallel Corpus-Trainable MT
Statistical MT (Lafferty, Vogel) Example-Based MT (Brown, Carbonell) AVENUE Instructible MT (Levin, Lavie,
Carbonell) Multi-Engine MT (Lavie, Frederking)
Speech-to-speech MT JANUS/DIPLOMAT/AVENUE (Waibel,
Frederking, Levin, Schultz, Vogel, Lafferty, Black, …)
We also Engage in: Tutoring Systems (Eskenazi, Callan) Linguistic Analysis (Levin, Mitamura…) Dialog Systems (Rudnicky, Waibel, …) Computational Biology
Protein structure/function (Carbonell, Langmead)
DNA seq/motifs (Yang, Xing, Rosenfeld) Complex System Design (Nyberg, Callan) Machine Learning (Carbonell, Lafferty, Yang,
Rosenfeld, Xing, Cohen,…) Question Answering (Nyberg, Mitamura,…)
How we do it at LTI Data-driven
methods Statistical learning Corpora-based
Examples: Statistical MT Example-based MT Text categorization Novelty detection Translingual IR
Knowledge-based Symbolic learning Linguistic analysis Knowledge
represent. Examples:
Interlingual MT Parsing &
generation Discourse modeling Language tutoring
MMR Ranking vs Standard IR
query
documents
MMR
IR
λ controls spiral curl
Adaptive Filtering over a Document Stream
On-topic documents
Test documents
Current document: On-topic?
Training documents (past)time
Off-topic documents
Unlabeled documents
RF
Topic 1
Topic 2
Topic 3…
Types of Machine Translation
Interlingua
Syntactic Parsing
Semantic Analysis
Sentence Planning
Text Generation
Source (Arabic)
Target(English)
Transfer Rules
Direct: SMT, EBMT
EBMT Example
English: I would like to meet her.Mapudungun: Ayükefun trawüael fey engu.
English: The tallest man is my father.Mapudungun: Chi doy fütra chi wentru fey ta inche ñi chaw.
English: I would like to meet the tallest man Mapudungun (new): Ayükefun trawüael Chi doy fütra chi wentru Mapudungun (correct): Ayüken ñi trawüael chi doy fütra wentruengu.
Ambiguity Makes MT Hard
Word Senses for “line” (52 senses in Random House English-Japanese Dictionary)
Power line – densen (電線 ) Subway line – chikatetsu ( 地下鉄 )
(Be) on line – onrain (オンライン ) (Be) on the line – denwachuu (電話中 ) Line up – narabu (並ぶ ) Line one’s pockets – kanemochi ni naru (金持ちになる ) Line one’s jacket – uwagi o nijuu ni suru (上着を二重にする ) Actor’s line – serifu (セリフ ) Get a line on – joho o eru (情報を得る )
CONTEXT: More is Better “The line for the new play extended
for 3 blocks.” “The line for the new play was
changed by the scriptwriter.” “The line for the new play got
tangled with the other props.” “The line for the new play better
protected the quarterback.”
Primary SequenceMNGTEGPNFY VPFSNKTGVV RSPFEAPQYY LAEPWQFSML AAYMFLLIML GFPINFLTLY VTVQHKKLRT PLNYILLNLA VADLFMVFGG FTTTLYTSLH GYFVFGPTGC NLEGFFATLG GEIALWSLVV LAIERYVVVC KPMSNFRFGE NHAIMGVAFT WVMALACAAP PLVGWSRYIP EGMQCSCGID YYTPHEETNN ESFVIYMFVV HFIIPLIVIF FCYGQLVFTV KEAAAQQQES ATTQKAEKEV TRMVIIMVIA FLICWLPYAG VAFYIFTHQG SDFGPIFMTI PAFFAKTSAV YNPVIYIMMN KQFRNCMVTT LCCGKNPLGD DEASTTVSKT ETSQVAPA
3D Structure
Folding
Complex function within network of proteins
Normal
PROTEINSSequence Structure Function
(Borrowed from: Judith Klein-Seetharaman)
Primary SequenceMNGTEGPNFY VPFSNKTGVV RSPFEAPQYY LAEPWQFSML AAYMFLLIML GFPINFLTLY VTVQHKKLRT PLNYILLNLA VADLFMVFGG FTTTLYTSLH GYFVFGPTGC NLEGFFATLG GEIALWSLVV LAIERYVVVC KPMSNFRFGE NHAIMGVAFT WVMALACAAP PLVGWSRYIP EGMQCSCGID YYTPHEETNN ESFVIYMFVV HFIIPLIVIF FCYGQLVFTV KEAAAQQQES ATTQKAEKEV TRMVIIMVIA FLICWLPYAG VAFYIFTHQG SDFGPIFMTI PAFFAKTSAV YNPVIYIMMN KQFRNCMVTT LCCGKNPLGD DEASTTVSKT ETSQVAPA
3D Structure
Folding
Complex function within network of proteins
Disease
PROTEINSSequence Structure Function
Predicting Protein Structures Protein Structure is a key determinant of protein function Crystalography to resolve protein structures experimentally in-vitro is
very expensive, NMR can only resolve very-small proteins The gap between the known protein sequences and structures:
3,023,461 sequences v.s. 36,247 resolved structures (1.2%) Therefore we need to predict structures in-silico
Linked Segmentation CRF
Node: secondary structure elements and/or simple fold Edges: Local interactions and long-range inter-chain and
intra-chain interactions L-SCRF: conditional probability of y given x is defined as
, , ,
1 1 , , ,,
1( ,..., | ,..., ) exp( ( , )) exp( ( , , , ))
i j G i j a b G
R R k k i i j l k i a i j a bV k lE
P f g yZ
y y y
y y x x x y x x y
Joint Labels
Discriminative Semi-Markov Model for Parallel Right-handed β-Helix Prediction
Structures A regular super secondary
structure with an an elongated helix whose successive rungs are composed of beta-strands
Conserved T2 turn
Computational importance Long-range interactions
Biological importance functions such as the bacterial
infection of plants, binding the O-antigen, antifreeze,...
Some LTI Accomplishments First large-scale web-spider (LYCOS) First speech-speech MT (JANUS) First high-accuracy text MT (KANT) First minority-language MT
(DIPLOMAT) First high-accuracy translingual IR First multidocument summarizer
(MMR)
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