Improving Patent Translation using Bilingual Term
Extraction and Re-tokenization for Chinese–JapaneseWei YANG and Yves LEPAGE
Graduate School of Information, Production and Systems
Waseda University

We describe a method to improve Chinese–Japanese statistical machine translation (SMT) of patents by re-tokenizing the training corpus with aligned bilingual multi-word terms.

√– consider one side is multi-word term (red )filtering by thresholds, ratio of lengths in words and components of thebilingual multi-word terms
Extractor not

Chinese and Japanese tokenization on patent sentences• Examples of terms in JPO1 Chinese–Japanese patent sentences are tokenized at different levels of granularity. Segmentation tools used are Stanford2 for Chinese and Juman3 for Japanese.
Language Sentence
Chinese 该/ 钽阳/极体 /通常/是/烧结/的/。
Japanese タンタル/陽極/ボディ /は/、/ 通常/、/ 焼結/さ/れて/いる/。

Correct ornot√
√√√√
∗
∗

×××××××

贴片/52/-/58/也/通过/导线/连接/到/系统/ 控制器 /30/。
パ ッ チ/52/〜/58/は/、/ま た/、/電 線/に/よ っ て/シJapanese
ステム/ コント/ローラ /30/に/接続/さ/れる/。
Chinese在/第 一/热/处 理/之 后/，/ 氧化物 / 半导体层 /变 成/ 缺氧 /的/氧 化 物/半 导体/，/即/，/电阻率/变得/更低/。
Japanese 酸化/物 / 半導体/層 /は/、/第/1/の/加 熱/処 理/後/に/ 酸素/欠乏 /型/と/なり/、/低/抵抗/化/する/。
Chinese××××××××××√
√
√
√
√
√
√
√
√
√
√
√
√
∗
∗
∗
∗

ChineseJapanese

P (t|s)P (s|t)接口 电子 线路インタフェース 電子 回路 0.923077 0.928571
顶盖 主体
キャッフ 本体
1.000000 0.833333
冷却 层
冷却 層
1.000000 0.951220
薄 膜片
薄膜 シート1.000000 1.000000
肺气肿
肺 気腫
0.818182 0.900000
激振 电极
主に 形成
0.861538 0.982456
芯片 级 控制 手机 模块 チップ レベル
1.000000 1.000000
废 热
变速 机

廃 熱
変速 機

0.844444 0.240506
1.000000 0.005988

壁部核酸
极板薄 膜
贵 金属

壁 部
核 酸
極 板
薄膜
貴金属

0.948247 0.677804
0.974392 0.956030
0.992000 1.000000
0.197531 0.058252
0.990548 0.984962

供油路输入 输出制动液甲醛
存储器 控制器
枢轴 板
切换 步骤亭飞行物总 能量 消耗量

給油 路
入出力
ブレーキ 液ホルム アルデヒド
メモリコントローラ
ピボットプレート
Handoverキオスク 端末
前記 飛行 体総計

1.000000 1.000000
0.952030 0.811321
0.985437 0.902222
0.997275 0.910448
0.968831 0.917589
0.977011 1.000000
1.000000 1.000000
1.000000 1.000000
1.000000 1.000000
1.000000 1.000000• We re-tokenize parallel training corpus with extracted bilingual multi-wordterms. Each multi-word term is re-tokenized (aligned) with markers.

Monolingual multi-word term extraction using C-value• The C-value is a commonly used automatic domain-independent method for
multi-word term extraction. This method has two main parts: a linguistic partand a statistical part.
– The linguistic pattern we use is the regular expression4:(Adjective|N oun)+ N oun– The statistical part, the measure of termhood, called the C-value, is givenby the following formula:

Experiments and results• Baseline (zh→ja) JPO corpus (lines)
training: 100,000, tuning: 500, test: 1,000 and 2,000• Monolingual multi-word term are extracted from training data:
Chinese: 81,618 and Japanese: 93,105• SMT experimentslog2 |a| · f (a)if a is not nested,1 P
C–value(a) =f (b) otherwiselog2 |a| f (a) −P (Ta) b∈Ta

(1)Chinese or Japanese sentences
在]P 该]DT 方法]NN 中]LC ,]PU 能够]VV 得到]VV 从]P心脏 ]NN 周 期]NN 内]LC 的]DEG 心收 缩]NN 期 ]NN 到]VV心]NN 舒张]VV 期]NN 之间]LC 的]DEG 血液]NN 移动]VV的]DEC 1]CD 个]M 以上]LC 的]DEG 图像]NN 。]PU

Extracted monolingual terms
心脏 周期‘cardiac cycle’心收缩 期‘systole’

この/指示詞 方法/名詞 に/助詞 おいて/動詞 は/助詞 、/特殊
心 臓/名名詞 周 期/名名詞 内/接尾辞 の/助詞 心/名名詞 収 縮/名名詞期 /名名詞 から/助詞 心/名名詞 拡張 /名名詞 期/名名詞 まで/助詞の/助詞 間/名詞 の/助詞 血液/名名詞 移動 /名名詞 の/助詞 1/名詞枚/接尾辞 以上/接尾辞 の/助詞 画像/名詞 が/助詞 得/動詞 られる/接尾辞 。/特殊心臓 周期‘cardiac cycle’
心 収縮 期‘systole’
心 拡張 期‘diastole’血液 移動‘blood moving’– We re-tokenize such candidate terms in the corpus by enforcing the extracted monolingual multi-word terms to be considered as one token.
Each candidate multi-word term is re-tokenized (aligned) with markers.

Bilingual multi-word term extraction• We use the open source implementation of the sampling-based approach,Anymalign [A. Lardilleux and Y. Lepage, 2009].
√– consider multi-word to multi-word terms (green )filtering by thresholds, ratio of lengths in words, and components of thebilingual multi-word terms– We use kanji-hanzi conversion method (Unihan Mapping Data, Langconv√
Traditional-Simplified Conversion data, Hanzi-kanji conversion) (blue ).
1– Baseline system (no re-tokenization)– Several systems based on re-tokenized training data using different number of bilingual multi-word terms.
Filtering by thresholds (a)Thresholds
≥ 0.0
≥ 0.1
≥ 0.2
Baseline (1,000)
≥ 0.3
≥ 0.4
≥ 0.5
≥ 0.6
≥ 0.7
≥ 0.8
≥ 0.9] of bilingual
multi-wordterms (a)52,785 (35%)
31,795 (52%)
27,916 (58%)
25,404 (63%)
23,515 (72%)
21,846 (76%)
20,248 (78%)
18,759 (79%)
17,311 (79%)
15,464 (80%)

BLEU p-value
32.44
32.23
32.00
32.35
33.08
32.77
33.02
33.32
32.85
33.25
33.20

> 0.05> 0.05> 0.05< 0.01< 0.05< 0.01< 0.01< 0.01< 0.01< 0.01

Filtering by thresholds (a) + the ratio of lengths + thecomponents (b) + kanji-hanzi conversion (c)] of bilingual multi-word ] of bilingual multi-word
BLEU p-value
terms (a + b)
terms (a + b + c)
48,239 (63%)
29,050 (68%)
25,562 (75%)
23,321 (78%)
21,644 (80%)
20,134 (85%)
18,691 (88%)
17,340 (88%)
16,001 (89%)
14,284 (92%)

49,474 (70%)
30,516 (78%)
27,146 (83%)
25,006 (83%)
23,424 (84%)
22,000 (88%)
20,679 (89%)
19,460 (90%)
18,265 (90%)
16,814 (93%)

Considering one side multi-word terms + filtering byconstraints (d) + (a + b + c)
] of combination of] of filtered one side]ofonesidemulti-wordThresholds
multi-word terms
multi-word terms (d)terms(a + b + c + d)
≥ 0.0
72,428 (2%)
27,116 (40%)
75,425 (64%)≥ 0.1
18,395 (7%)
7,570 (55%)
37,059 (78%)≥ 0.2
14,179 (12%)
6,031 (62%)
32,224 (85%)≥ 0.3
11,849 (15%)
5,161 (70%)
29,280 (90%)≥ 0.4
10,259 (17%)
4,537 (76%)
27,125 (90%)≥ 0.5
9,069 (17%)
4,050 (76%)
25,270 (90%)≥ 0.6
7,875 (30%)
3,575 (76%)
23,522 (93%)≥ 0.7
6,900 (30%)
3,088 (80%)
21,874 (93%)≥ 0.8
6,026 (30%)
2,726 (80%)
20,318 (93%)≥ 0.9
5,062 (30%)
2,275 (82%)
18,484 (95%)

BLEU p-value
32.55
33.36
33.20
33.41
33.37
33.63
34.27
33.90
33.85
33.75

Test is 2,000 sentences (zh) Evaluation result
Baseline
32.29
Re-tokenization
33.61(p-value < 0.01)

http://lotus.kuee.kyoto-u.ac.jp/WAT/patent/index.html
http://nlp.stanford.edu/software/segmenter.shtml
3
http://nlp.ist.i.kyoto-u.ac.jp/index.php?JUMAN
4Pattern for Chinese: (JJ|N N )+ N N , pattern for Japanese: (形容詞 | 名詞)+ 名詞. ‘JJ’ and ‘形容詞’ are codes for adjectives, ‘NN’ and ‘名詞’ are codes for nouns in the Chinese and the Japanese taggers that we use.
2The 3rd Workshop on Asian Translation (WAT2016), Osaka, Japan, 2016

33.19
33.09
33.12
32.35
33.25
33.31
33.23
33.75
33.41
33.38
33.43

> 0.05< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01< 0.05< 0.05< 0.05< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01

The 3rd Workshop on Asian Translation (WAT2016), Osaka, Japan, 2016