Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2015
Katsuhito Sudoh and Masaaki Nagata, NTT Communication Science Laboratories, Japan
Overview

Rule-based pre-ordering

Dependency-based pre-ordering for Zh-Ja MT- Patent-adapted in-house dependency parser- Two-types of pre-ordering:* Rule-based, Head Final Chinese (Han+ 2012)* Data-driven, Learning to Rank (Yang+ 2012)- Rule-based system is better, comparable to T2S

Reordering into head-final order in Japanese(En-Ja: Isozaki+ 2012, Zh-Ja: Han+ 2012)

Standard Moses Phrase-based MT

Base rule: Moving a head word after its modifiers- Kneser-Ney phrase-table score smoothing

Exceptions (placed after their head words):
AS (aspect particle), SP (sentence-final particle)
PU (punctuation), CC (coordinating conjunction)
IJ (interjection), “不” (negation), “等” (”etc.”)

Syntactic Analysis

Exception- Joint sequential labeling (Suzuki+ 2012)

我PN[Dependency parsing (untyped)]

不AD

看VV电视
NN- MGIZA word alignment, g-d-f-a symal- Word 5-gram LM with Kneser-Ney smoothing- Distortion limit: 9 (chosen over 0,3,6,9)- Weights chosen over 5 indep. MERT runs

Results

Head Finalization[Word segmentation & POS tagging]我 电视 看PNNNVV不AD

Comparable to T2S baseline
Rule-based is better than data-driven
Table 2: Official evaluation results

Pros: stability, domain independence (?)
Cons: effort for rule management- Second-order graph-based parsing[Semi-supervised learning] (Suzuki+ 2009)

Data-driven pre-ordering- Labeled: 31K sents. (news), 35K sents. (patents)- Unlabeled: 9GB (news), 100GB (patents)
Table 1: Performance in Chinese syntactic analysis

Accuracy(F0 / UAS)

SMT setup

Word seg.

POS

Dep.

0.927

0.855

0.927

Reordering by reranking a head & its modifiers(Yang+ 2012)- Implemented with Ranking SVM* Features: - surface/POS (head & modifier)- head surface/POS (h & m)
- modifier surface/POS (head)- span surfaces/POSs (modifier)- relative position (h & m)

References:Han, Dan et al., Head Finalization Reordering for Chinese-to-Japanese MT,
Proc. SSST-6 (2012)
Hoshino, Sho et al., Discriminative Preordering Meets Kendall’s tau Maximization, Proc. ACL (2015)* Reordering oracles are determined by maximizing Kendall’s tau criterion (Hoshino+ 2015)

Isozaki, Hideki et al, HPSG-Based Preprocessing for English-to-JapaneseTranslation, ACM TALIP No.11 Vol.3 (2012)

1432

我不看电视

Suzuki, Jun et al, An Empirical Study of Semi-supervised Structured Conditional Models for Depenedency Parsing, Proc. EMNLP (2009)
Suzuki, Jun et al., 拡張ラグランジュ緩和を用いた同時自然言語解析法,
Proc. NLP (2012) [in Japanese]
Yang, Nan et. al., A Ranking-based Approach to Word Reordering for SMT,Proc. ACL (2012)PNADVVNN

我 电视 看PNNNVV不AD

Pros: no special effort, target adaptability
Cons: instability, noisy auto. word alignment

HumanRIBESBLEUBL PBMT

n/a

0.781

0.382

BL T2S

20.75

0.814

0.394

Rule-based

16.25

0.822

0.406

Data-driven

8.00

0.812

0.399

Conclusion
Pre-ordering is a deterministic approx. of T2S--- good in efficiency with some loss in accuracy> forest-based pre-ordering, pre-ordering lattice
Rule-based pre-ordering works robustly--- due to head-final nature in Japanese
Data-driven pre-ordering is still challenging...
--- difficulty in word alignment, non-parallelism--- constituent or dependency structures?
Remained patent MT issues:- Context awareness (consistency)- Domain awareness (lexical choice)