December 11-16 2016 Osaka Japan

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Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for Chinese–Japanese Wei Yang IPS, Waseda University 2-7 Hibikino, Wakamatsu Kitakyushu Fukuoka,, Japan kevinyoogi@akene.waseda.jp Yves Lepage IPS, Waseda University 2-7 Hibikino, Wakamatsu Kitakyushu Fukuoka,, Japan yves.lepage@waseda.jp Abstract Unlike European languages, many Asian languages like Chinese and Japanese do not have typographic boundaries in written system. Word segmentation (tokenization) that break sentences down into individual words (tokens) is normally treated as the first step for machine translation (MT). For Chinese and Japanese, different rules and segmentation tools lead different segmentation results in different level of granularity between Chinese and Japanese. To improve the translation accuracy, we adjust and balance the granularity of segmentation results around terms for Chinese–Japanese patent corpus for training translation model. In this paper, we describe a statistical machine translation (SMT) system which is built on re-tokenized Chinese–Japanese patent training corpus using extracted bilingual multi-word terms. 1 Introduction China and Japan are producing a large amount of patents in their respective languages. Making Chinese patents available in Japanese, and Japanese patents in Chinese is an important task for increasing economical development in Asia and international world. The translation of patents is a key issue that should be helped by the use of SMT. Word segmentation is normally treated as the first step for SMT between Chinese and Japanese. Patents contain large amounts of domain-specific terms in words or multi-word expressions. This brings up the question of word segmentation: we may not want to tokenize terms in specific domains in patents. But we cannot control the tokenization of the multi-word terms: a large number of multi-word terms are always segmented into several single-word terms in one language but may not be segmented in another language, or some of the multi-word terms in two languages have different levels of granularity in segmentation because of different conventions of segmentation in different languages. The related work by Chang et al. (2008) shows that segmentation granularity of Chinese word segmentation affects the translation accuracy and that it is very important for MT. In (Chu et al., 2013), for improving the translation accuracy of scientific papers, they make use of a constructed mapping table for adjusting Chinese segmentation results according to Japanese segmentation based on characters shared between Chinese and Japanese. In our work, we focus on terms and patent segmentation and translation. To improve SMT translation accuracy, we change and adjust the segmentation for terms using extracted bilingual multi-word terms for both languages (not only for Chinese or Japanese). Frantzi et al. (2000) describes a combination of linguistic and statistical methods (C-value/NC-value) for the automatic extraction of multi-word terms from English corpora. In (Mima and Ananiadou, 2001), it is showed that the C-/NC-value method is an efficient domain-independent multi-word term recognition not only in English but in Japanese as well. In this paper, we adopt the C-value method to extract monolingual multi-word terms in Chinese and Japanese, and combine it with the sampling-based alignment method (Lardilleux and Lepage, 2009) and kanji-hanzi conversion method for bilingual multi-word term extraction. We build SMT systems based on re-tokenized Chinese–Japanese patent training corpus using the extracted bilingual multi-word terms. Place licence statement here for the camera-ready version, see Section ?? of the instructions for preparing a manuscript. 194 Proceedings of the 3rd Workshop on Asian Translation, pages 194–202, Osaka, Japan, December 11-17 2016.
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/。 Meaning ‘Tantalum anode body are usually sintered.’ Chinese 贴片 /52/-/58/ 也 / 通过 / 导线 / 连接 / 到 / 系统 / 控制器 /30/。 Japanese パッチ/52/〜/58/は/、/また/、/ 電線 /に/よって/システム/ コント/ローラ /30/に/ 接続 /さ/れる/。 Meaning ‘Patches 52-58 are also connected to the system controller 30 by wires.’ 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/。 Meaning ‘The oxide semiconductor layer becomes an oxygen-deficient type after the first heat treatment, namely, the resistivity becomes lower.’ Chinese 这 / 是 / 因为 / 水 / 与 / 异氰 / 酸酯基 / 反应 /,/ 以 / 形成 / 脲键 /。 Japanese Meaning これ/は/、/ 水 /と/ イソシアネート/ 基 /が/ 反応 /する/こと/で/、/ ウレア/ 結合 /が/ 生じる/ため/である/。 ‘This is because of the reaction between water and isocyanate groups for forming urea bonds.’ Chinese 在 / 检测 / 出 / 的 / 放射线 / 量 / 小于 / 阈值 / 的 / 情况 / 下 /,/ 为 / 否定 / 判断 /,/ 从而 / 进入 / 到 / 步骤 /110/。 Japanese Meaning 検知 /した/ 放射 / 線量 /が/ 閾値 / 未満 /である/ 場合 /は/、/ 否定 /さ/れて/ステップ/110/へ/ 進む/。 ‘In the case where the radiation dose detected is less than the threshold, it is considered as the negative judgment, then go to step 110.’ Chinese 因而 /,/ 在 / 本 / 实施 / 方式 / 中 /,/ 能够 / 高效率 / 地 / 进行 / 关于 / 肺气肿 / 的 / 图像 / 诊断 / 的 / 支援 /。 Japanese Meaning 従って/、/ 本 / 実施 / 形態 /で/は/、/ 肺 / 気腫 /に/ 関する/ 画像 / 診断 /の/ 支援 /を/ 効率 / 良く/ 行なう/こと/が/できる/。 ‘Thus, in this embodiment, the support on the image diagnosis of emphysema can be performed efficiently.’ Figure 1: Examples of Chinese–Japanese patent segmentation. Terms in different languages are tokenized at different levels of granularity. Segmentation tools used are Stanford for Chinese and Juman for Japanese. The words given in the box are the multi-word terms or single-word terms in Chinese or Japanese. The words in the same color have corresponding translation relations between two languages. 2 Word Segmentation for Chinese–Japanese Patent Corpus Figure 1 gives the examples for Chinese–Japanese patent sentences which are tokenized at different levels of granularity based on different segmentation tools. For instance, the multi-word term 钽阳 / 极体 (‘tantalum anode body’) in Chinese has a translation relation with the multi-word タンタル/ 陽極 /ボディ in Japanese, but actually, they do not have any correspondence in word-to-word alignments. Similar examples are given as 异氰 / 酸酯基 (‘isocyanate group’) in Chinese and イソシアネート/ 基 in Japanese, 放射线 / 量 (‘radiation dose’) in Chinese and 放射 / 線量 in Japanese. Another case is that some terms are multi-word terms in one language but single-word terms in another language. For instance, the single-word term 肺气肿 (‘emphysema’) in Chinese and the multi-word term 肺 / 気腫 in Japanese. For keeping the direct and exact translations between Chinese and Japanese terms, we intend to re-tokenize Chinese–Japanese parallel sentences center around bilingual multi-word terms. As such, correspondence and meaning of terms come into focus when adjusting word tokenization granularity. To do this, we extract bilingual multi-word terms from an existing Chinese–Japanese training corpus, then we build SMT systems based on the re-tokenized training corpus using these extracted bilingual multi-word terms by enforcing them to be considered as one token. 3 Chinese–Japanese Bilingual Multi-word Term Extraction In this section, we describe a bilingual multi-word term extraction method used in our work. We combine using C-value for monolingual multi-word extraction with the sampling-based alignment method and kanji-hanzi conversion method for bilingual multi-word term extraction. 195
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WAT 2016 The 3rd Workshop on Asian
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Preface Many Asian countries are ra
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Technical Collaborators Luis Fernan
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Table of Contents Overview of the 3
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Conference Program December 12, 201
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December 12, 2016 (continued) 12:00
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Overview of the 3rd Workshop on Asi
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LangPair Train Dev DevTest Test JPC
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ASPEC JPC IITB BPPT pivot System ID
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3.6 Tree-to-String Syntax-based SMT
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• Use of other resources in addit
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ASPEC JPC BPPT IITBC pivot Team ID
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ut the adequacy is worse. As for AS
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Figure 3: Official evaluation resul
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Figure 11: Official evaluation resu
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Figure 13: Official evaluation resu
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Annotator A Annotator B all weighte
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Kyoto-U 2 bjtu nlp UT-KAY 1 UT-KAY
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Online B IITP-MT EHR Online A ≫
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SYSTEM ID ID METHOD OTHER RESOURCES
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SYSTEM ID ID METHOD OTHER BLEU RIBE
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Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
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Translation of Patent Sentences wit
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Chinese sentences contain fewer tha
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Figure 3: NMT decoding with technic
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Table 1: Automatic evaluation resul
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Figure 6: Example of correct transl
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K. Papineni, S. Roukos, T. Ward, an
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Table 1: Examples of title, ingredi
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text. In this section, we explain t
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Table 3: Automatic evaluation BLEU/
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Table 5: Number of fluency errors i
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Kenneth Heafield. 2011. Kenlm: Fast
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under-studied. MorphInd, a morphana
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dimensions of 150 units in second h
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distribution as received in JPO ade
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Domain Adaptation and Attention-Bas
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Once s M , which represents the ent
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3.4.2 Ensemble of Attention Scores
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Type Count Ratio (A) Correct 76 30.
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Minh-Thang Luong, Hieu Pham, and Ch
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Pair 1 Japanese [[ A ][ B ][ C ]
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ID n-grams len freq m1 prevent, | 1
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prediction accuracy with respect to
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Reference: [ A To provide] [ B a to
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Philipp Koehn. 2004. Statistical si
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Therefore, we propose to use phrase
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Figure 2: An NRM considering phrase
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Mono Swap D right D left Acc. The r
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Method ja-en en-ja BLEU RIBES WER B
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Peng Li, Yang Liu, and Maosong Sun.
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Figure 1: The framework of NMT. Whe
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For long sentence, we discarded all
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As shown in the above tables and fi
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Translation systems and experimenta
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The number of TM training sentence
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where, f, p and e means a source, p
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former system can translate more th
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Lexicons and Minimum Risk Training
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2.2 Parameter Optimization If we de
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ML (λ=0.0) ML (λ=0.8) MR (λ=0.0)
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Graham Neubig. 2013. Travatar: A fo
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Feature Space Common (h ) Domain 1
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Preprocessing Training Translation
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JPC ASPEC LM Method Ja-En En-Ja Ja-
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Translation Using JAPIO Patent Corp
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4 Results Table 1 shows official ev
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Table 3: Examples of translation er
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An Efficient and Effective Online S
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#$ #%$ #$ !""
Page 157 and 158: This formula requires a context of
Page 159 and 160: Sentence Segmenter Parameters Dev.
Page 161 and 162: a meaningful baseline for related r
Page 163 and 164: Similar Southeast Asian Languages:
Page 165 and 166: τ around 0.71, and the Malay-Indon
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Page 171 and 172: Integrating empty category detectio
Page 173 and 174: 3 Preordering with empty categories
Page 175 and 176: We performed the tests under two co
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Page 179 and 180: Kenneth Heafield. 2011. Kenlm: Fast
Page 181 and 182: Figure 1: Overview of KyotoEBMT. Th
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Page 185 and 186: 3.6 Ensembling Ensembling has previ
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Page 189 and 190: Character-based Decoding in Tree-to
Page 191 and 192: where W s ∈ R d×d is a matrix an
Page 193 and 194: Model BLEU (BP) RIBES Character-bas
Page 195 and 196: The word-based NMT models cannot us
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Page 203 and 204: 3 Results Team Other Resources Syst
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Page 207: Yonghui Wu, Mike Schuster, Zhifeng
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Page 213 and 214: 4 Experiments and Results 4.1 Chine
Page 215 and 216: 6 Conclusion and Future Work In thi
Page 217 and 218: Controlling the Voice of a Sentence
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Page 237 and 238: Residual Stacking of RNNs for Neura
Page 239 and 240: 2.2 Generalized Minimum Bayes Risk
Page 241 and 242: 5.0 4.5 4.0 Baseline model Enlarged
Page 243: Felix Hill, Kyunghyun Cho, Sebastie
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December 11-16 2016 Osaka Japan

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