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Filtering by thresholds P (a) Filtering by thresholds P (a) + the ratio of lengths + the components (b) + kanji-hanzi conversion (c) Thresholds P ♯ of bilingual ♯ of bilingual multi-word ♯ of bilingual multi-word multi-word BLEU p-value terms (a + b) terms (a + b + c) terms (a) BLEU p-value ≥ 0.0 52,785 (35% ) 32.63 > 0.05 48,239 (63%) 49,474 (70%) 33.15 < 0.01 ≥ 0.1 31,795 (52%) 32.76 > 0.05 29,050 ( 68%) 30,516 (78% ) 33.10 < 0.01 ≥ 0.2 27,916 (58%) 32.57 > 0.05 25,562 (75%) 27,146 (83%) 33.05 < 0.01 Baseline - 32.38 - - - 32.38 - ≥ 0.3 25,404 (63%) 33.07 < 0.01 23,321 (78% ) 25,006 (83%) 33.21 < 0.01 ≥ 0.4 23,515 (72%) 32.92 < 0.01 21,644 (80%) 23,424 (84%) 33.29 < 0.01 ≥ 0.5 21,846 (76%) 33.05 < 0.01 20,134 (85%) 22,000 (88%) 33.38 < 0.01 ≥ 0.6 20,248 (78%) 33.61 < 0.01 18,691 (88%) 20,679 (89%) 33.93 < 0.01 ≥ 0.7 18,759 (79%) 32.92 < 0.01 17,340 (88%) 19,460 (90%) 33.43 < 0.01 ≥ 0.8 17,311 (79%) 33.34 < 0.01 16,001 (89%) 18,265 (90%) 33.41 < 0.01 ≥ 0.9 15,464 (80%) 33.47 < 0.01 14,284 (92%) 16,814 (93%) 33.52 < 0.01 Table 6: Evaluation results in BLEU for Chinese to Japanese translation based on re-tokenized training corpus using different thresholds (a); based on combination of the ratio of lengths + the components (b) with kanji-hanzi conversion (c). In all experiments, the same data sets are used, the only difference being whether the training data is retokenized or not with bilingual multi-word terms. Table 6 shows the evaluation of the results of Chineseto-Japanese translation in BLEU scores (Papineni et al., 2002). Compared with the baseline system, for the training corpus re-tokenized with further filtering combined with kanji-hanzi conversion results (a +b + c), we obtain significant improvements in all thresholds. We obtain 1.55 BLEU point (threshold of 0.6) improvements compare with the baseline system. In this case, 20,679 re-tokenized terms are used. It is also improve 0.3 BLEU point comparing with the case of the bilingual terms are filtered only by thresholds (a). We then test 2,000 sentences based on this best SMT system and the baseline system. We obtain a significant BLEU score with 33.61 compare with the baseline system 32.29 (p-value < 0.01). Figure 2 gives an example of improvement in Chinese-to-Japanese translation. Thanks to our method, re-tokenizing the training corpus with bilingual multi-word terms gave a better translation accuracy (BLEU=15.92) of the test sentence given in this example. Re-tokenizing and grouping the bilingual multi-word term together increased the probability of multi-word term to multi-word term translation, i.e., “ 免疫测定方法 ” to “ 免疫測定方法 ” (‘immunoassay’) in this example. This prevents the separated 1-to-1 or 2-to-2 gram translation of isolated source words in inappropriate order or position, like “ 免疫 ” to “ 免疫 ” (‘immunity’) and “ 测定方法 ” to “ 測定方法 ” (‘measuring method’). In this example, re-tokenization of the training corpus with extracted bilingual multi-word terms induced a direct and exact translation. Test sentence (Chinese): 作为 (0) 测定 (1) 被 (2) 检液 (3) 中 (4) 的 (5) 特定 (6) 成分 (7) 的 (8) 方法 (9) ,(10) 存在 (11) 许多 (12) 利用 (13) 了 (14) 抗原 (15) 抗体 (16) 反应 (17) 的 (18) 免疫 (19) 测定 (20) 方法 (21) 。(22) Baseline (BLEU=15.92): 測定 |1-1| は |2-2| 、 |10-11| 多くの |12-12| 方法 |9-9| として |0-0| は |13-13| 、 |14-14| 抗原抗体 |15-16| 反応の |17-18| 免疫 |19-19| 检液 |3-3| 内の |4-5| 特定の |6-6| 成分 |7-7| の |8-8| 測定 |20-20| 方法 |21-21| 。 |22-22| Re-tokenizing training corpus with bilingual multi-word terms (BLEU=25.54): Reference (Japanese): 測定 |1-1| が |2-2| 液 |3-3| 内の |4-5| 特定の |6-6| 成分の |7-8| 方法 |9-9| として |0-0| 、 |10-11| 抗原抗体反応させ |15-17| の |18-18| 免疫測定方法 |19-21| については多数の |12-12| 利用 |13-13| されている |14-14| 。 |22-22| 被検液中の特定成分を測定する方法として、抗原抗体反応を利用した免疫測定方法が数多く存在する。 Figure 2: Example of Chinese-to-Japanese translation improvement. The numbers in the parentheses show the position of the word in the test sentence. The numbers in the vertical lines show for the translation result (Japanese), the position of the n-gram used in the test sentence (Chinese). 200
6 Conclusion and Future Work In this paper, we described a Chinese–Japanese SMT system for the translation of patents built on a training corpus re-tokenized using automatically extracted bilingual multi-word terms. We extracted monolingual multi-word terms from each part of the Chinese–Japanese training corpus by using the C-value method. For extraction of bilingual multi-word terms, we firstly re-tokenized the training corpus with these extracted monolingual multi-word terms for each language. We then used the sampling-based alignment method to align the re-tokenized parallel corpus and only kept the aligned bilingual multi-word terms by setting different thresholds on translation probabilities in both directions. We also used kanji-hanzi conversion to extract bilingual multi-word terms which could not be extracted using thresholds or only one side is multi-word terms. We did not use any other additional corpus or lexicon in our work. Re-tokenizing the parallel training corpus with the results of the combination of the extracted bilingual multi-word terms led to statistically significant improvements in BLEU scores for each threshold. We then test 2,000 sentences based on the SMT system with the highest BLEU score (threshold of 0.6). We also obtained a significant improvement in BLEU score compare with the baseline system. In this work, we limited ourselves to the cases where multi-word terms could be found in both languages at the same time, e.g., 血糖正常水平 (Chinese) 正常血糖レヘル (Japanese) (‘normal blood glucose level’), and the case where multi-word terms made up of hanzi/kanji are recognized in one of the languages, but not in the other language. e.g. 癌细胞 (Chinese) 癌細胞 (Japanese) (‘cancer cell’) or 低压 (Chinese) 低圧 (Japanese) (‘low tension’). Manual inspection of the data allowed us to identify a third case. It is the case where only one side is recognized as multi-word term, but the Japanese part is made up of katakana or a combination of kanji and katakana, or the Japanese part is made up of kanji but they do not share the same characters with Chinese after kanji-hanzi conversion. Such a case is, e.g., 碳纳米管 (Chinese) カーボンナノチューブ (Japanese) (‘carbon nano tube’) and 控制器 (Chinese) コントローラ (Japanese) (‘controller’), or 逆变器 (Chinese) インバータ (Japanese) (‘inverter’) or still 乙酸乙酯 (Chinese) 酢酸エチル (Japanese) (‘ethyl acetate’) and 尿键 (Chinese) ウレア結合 (Japanese) (‘urea bond’) , or 氧化物 (Chinese) 酸化物 (Japanese) (‘oxide’) and 缺氧 (Chinese) 酸素欠乏 (Japanese) (‘oxygen deficit’). In a future work, we intend to address this third case and expect further improvements in translation results. Acknowledgements References Pi-Chuan Chang, Michel Galley, and Christopher D Manning. 2008. Optimizing Chinese word segmentation for machine translation performance. In Proceedings of the third workshop on statistical machine translation, pages 224–232. Association for Computational Linguistics. Chenhui Chu, Toshiaki Nakazawa, Daisuke Kawahara, and Sadao Kurohashi. 2013. Chinese-Japanese machine translation exploiting Chinese characters. ACM Transactions on Asian Language Information Processing (TALIP), 12(4):16. Katerina Frantzi, Sophia Ananiadou, and Hideki Mima. 2000. Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal on Digital Libraries, 3(2):115–130. Kenneth Heafield. 2011. Kenlm: Faster and smaller language model queries. In Proceedings of the Sixth Workshop on Statistical Machine Translation, pages 187–197. Association for Computational Linguistics. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, and et al. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (ACL 2007), pages 177–180. Association for Computational Linguistics. Adrien Lardilleux and Yves Lepage. 2009. Sampling-based multilingual alignment. In Recent Advances in Natural Language Processing, pages 214–218. 201
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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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#$ #%$ #$ !""
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This formula requires a context of
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Sentence Segmenter Parameters Dev.
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a meaningful baseline for related r
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Page 171 and 172: Integrating empty category detectio
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Page 179 and 180: Kenneth Heafield. 2011. Kenlm: Fast
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Page 243: Felix Hill, Kyunghyun Cho, Sebastie
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