1. Eliciting a corpus of word-aligned phrases for MT
Lori Levin, Alon Lavie, Jaime Carbonell
Erik Peterson, Alison Alvarez
Language Technologies Institute
Carnegie Mellon University

2. Introduction
Problem: Building Machine Translation systems for languages with scarce resources:
Not enough data for Statistical MT and Example-Based MT
Not enough human linguistic expertise for writing rules
Approach:
Elicit high quality, word-aligned data from bilingual speakers
Learn transfer rules from the elicited data

3. Modules of the AVENUE/MilliRADD rule learning system and MT system
Learning Module
Transfer Rules
{PP,4894} ;;Score: PP::PP [NP POSTP] -> [PREP NP] ((X2::Y1) (X1::Y2))
Translation Lexicon
Run Time Transfer System
Lattice
Decoder
English Language Model
Word-to-Word Translation Probabilities
Word-aligned elicited data

4. Outline Demo of elicitation interface
Description of elicitation corpus

5. Demo of Elicitation Tool
Speaker needs to be bilingual and literate: no other knowledge necessary
Mappings between words and phrases: Many-to-many, one-to-none, many-to-none, etc.
Create phrasal mappings
Fonts and character sets:
Including Hindi, Chinese, and Arabic
Add morpheme boundaries to target language
Add alternate translations
Notes and context

6. English-Chinese Example

7. English-Hindi Example

8. Spanish-Mapudungun Example

9. English-Arabic Example

10. Testing of Elicitation Tool
DARPA Hindi Surprise Language Exercise
Around 10 Hindi speakers
Around 17,000 phrases translated and aligned
Elicitation corpus
NPs and PPs from Treebanked Brown Corpus

11. Elicitation Corpus: Basic Principles
Minimal pairs
Syntactic compositionality
Special semantic/pragmatic constructions
Navigation based on language typology and universals
Challenges

12. Elicitation Corpus: Minimal Pairs
Eng: I fell.
Sp: Caí
M: Tranün
Eng: You (John) fell.
Sp: Tu (Juan) caiste
M: Eymi tranimi (Kuan)
Eng: You (Mary) fell. ;;
Sp: Tu (María) caiste
M: Eymi tranimi (Maria)
Eng: I am falling.
Sp: Estoy cayendo
M: Tranmeken
Eng: You (John) are falling.
Sp: Tu (Juan) estás cayendo
M: Eimi(Kuan) tranmekeymi
Mapudungun: Spoken by around one million people in Chile and Argentina.

13. Using feature vectors to detect minimal pairs
np1:(subj-of cl1).pro-pers.hum.2.sg. masc.no-clusn.no-def.no-alien
cl1:(subj np1).intr-ag.past.complete
Eng: You (John) fell.
Sp: Tu (Juan) caiste
M: Eymi tranimi (Kuan)
np1:(subj-of cl1).pro-pers.hum.2.sg. fem.no-clusn.no-def.no-alien
Eng: You (Mary) fell. ;;
Sp: Tu (María) caiste
M: Eymi tranimi (Maria)
Inventory of features is based on fieldwork checklists: Comrie and Smith; Boqiaux and Thomas.
Feature vectors can be extracted from the output of a parser for English or Spanish. (Except for features that English and Spanish do not have…)

14. Syntactic Compositionality
The tree
The tree fell.
I think that the tree fell.
We learn rules for smaller phrases
E.g., NP
Their root nodes become non-terminals in the rules for larger phrases.
E.g., S containing an NP
Meaning of a phrase is predictable from the meanings of the parts.

15. Special Semantic and Pragmatic Constructions
Meaning may not be compositional
Not predictable from the meanings of the parts
May not follow normal rules of grammar.
Suggestion: Why not go?
Word-for-word translation may not work.
Tend to be sources of MT mismatches
Comparative:
English: Hotel A is [closer than Hotel B]
Japanese: Hoteru A wa [Hoteru B yori] [tikai desu]
Hotel A TOP Hotel B than close is
“Closer than Hotel B” is a constituent in English, but “Hoteru B yori tikai” is not a constituent in Japanese.

16. Examples of Semantic/Pragmatic Categories
Speech Acts: requests, suggestions, etc.
Comparatives and Equatives
Modality: possibility, probability, ability, obligation, uncertainty, evidentiality
Correllatives: (the more the merrier)
Causatives
Etc.

17. A Challenge: Combinatorics
Person (1, 2, 3, 4)
Number (sg, pl, du, paucal)
Gender/Noun Class (?)
Animacy (animate/inanimate)
Definiteness (definite/indefinite)
Proximity (near, far, very far, etc.)
Inclusion/exclusion
Multiply with: tenses and aspects (complete, incomplete, real, unreal, iterative, habitual, present, past, recent past, future, recent future, non-past, non-future, etc.)
Multiply with verb class: agentive intransitive, non-agentive intransitive, transitive, ditransitive, etc.
(Case marking and agreement may vary with verb tense, verb class, animacy, definiteness, and whether or not object outranks subject in person or animacy.)

18. Solutions to Combinatorics
Generate paradigms of feature vectors, and then automatically generate sentences to match each feature vector.
Use known universals to eliminate features: e.g., Languages without plurals don’t have duals.

19. Navigation through the corpus
Initial diagnostics:
Does the language mark number on nouns or in agreement with verbs?
Sentence selection:
Based on initial diagnostics
Based on principles and universals
E.g., languages that don’t have plurals don’t have duals
So that the informant sees very few sentences that are not relevant for his/her language

20. Other Challenges of Computer Based Elicitation
Inconsistency of human translation and alignment
Bias toward word order of the elicitation language
Need to provide discourse context for given and new information
How to elicit things that aren’t grammaticalized in the elicitation language:
Evidential: I see that it is raining/Apparently it is raining/It must be raining.
Context: You are inside the house. Your friend comes in wet.