Building a Word-Aligned Phrases Corpus for Machine Translation Systems

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. Word-aligned elicited data English Language Model Learning Module Run Time Transfer System Word-to-Word Translation Probabilities Transfer Rules {PP,4894};;Score:0.0470PP::PP [NP POSTP] -> [PREP NP]((X2::Y1)(X1::Y2)) Decoder Lattice Translation Lexicon Modules of the AVENUE/MilliRADD rule learning system and MT system

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. 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 Elicitation Corpus: Minimal Pairs 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 • cl1:(subj np1).intr-ag.past.complete • 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.