December 11-16 2016 Osaka Japan

More documents

Recommendations

Info

Figure 5: Example of correct translations produced by the proposed NMT system with SMT technical term translation (compared with baseline SMT) uation, we compare each of translations produced by the baseline SMT with that produced by the two versions of the proposed NMT systems, and judge which translation is better, or whether they are with comparable quality. The score of pairwise evaluation is defined by the following formula, where W is the number of better translations compared to the baseline SMT, L the number of worse translations compared to the baseline SMT, and T the number of translations having their quality comparable to those produced by the baseline SMT: score = 100 × W − L W + L + T The score of pairwise evaluation ranges from −100 to 100. In the JPO adequacy evaluation, Chinese translations are evaluated according to the quality evaluation criterion for translated patent documents proposed by the Japanese Patent Office (JPO). 13 The JPO adequacy criterion judges whether or not the technical factors and their relationships included in Japanese patent sentences are correctly translated into Chinese, and score Chinese translations on the basis of the percentage of correctly translated information, where the score of 5 means all of those information are translated correctly, while that of 1 means most of those information are not translated correctly. The score of the JPO adequacy evaluation is defined as the average over the whole test sentences. Unlike the study conducted Nakazawa et al. (Nakazawa et al., 2015), we randomly selected 200 sentence pairs from the test set for human evaluation, and both human evaluations were conducted using only one judgement. Table 2 shows the results of the human evaluation for the baseline SMT, the baseline NMT, and the proposed NMT system. We observed that the proposed system achieved the best performance for both pairwise evaluation and JPO adequacy evaluation when we replaced technical term tokens with SMT technical term translations after decoding the source sentence with technical term tokens. Throughout Figure 5∼Figure 7, we show an identical source Japanese sentence and each of its translations produced by the two versions of the proposed NMT systems, compared with translations produced by the three baselines, respectively. Figure 5 shows an example of correct translation produced by the proposed system in comparison to that produced by the baseline SMT. In this example, our model correctly translates the Japanese sentence into Chinese, whereas the translation by the baseline SMT is a translation error with several erroneous syntactic structures. As shown in Figure 6, the second example highlights that the proposed NMT system of Section 4.2 can correctly translate the Japanese technical term “ ”(laminated wafer) to the Chinese technical term “ ”. The translation by the baseline NMT is a translation error because of not only the erroneously translated unknown token but also the Chinese word “ ”, which is not appropriate as a component of a Chinese technical term. Another example is shown in Figure 7, where we compare the translation of a reranking SMT 1,000-best 13 https://www.jpo.go.jp/shiryou/toushin/chousa/pdf/tokkyohonyaku_hyouka/01.pdf (in Japanese) 54
Figure 6: Example of correct translations produced by the proposed NMT system with SMT technical term translation (compared to decoding with the baseline NMT) Figure 7: Example of correct translations produced by reranking the 1,000-best SMT translations with the proposed NMT system (compared to reranking with the baseline NMT) translation produced by the proposed NMT system with that produced by reranking with the baseline NMT. It is interesting to observe that compared with the baseline NMT, we obtain a better translation when we rerank the 1,000-best SMT translations using the proposed NMT system, in which technical term tokens represent technical terms. It is mainly because the correct Chinese translation “ ”(wafter) of Japanese word “ ” is out of the 40K NMT vocabulary (Chinese), causing reranking with the baseline NMT to produce the translation with an erroneous construction of “noun phrase of noun phrase of noun phrase”. As shown in Figure 7, the proposed NMT system of Section 4.3 produced the translation with a correct construction, mainly because Chinese word “ ”(wafter) is a part of Chinese technical term “ ”(laminated wafter) and is replaced with a technical term token and then rescored by the NMT model (with technical term tokens “TT 1 ”, “TT 2 ”, ...). 6 Conclusion In this paper, we proposed an NMT method capable of translating patent sentences with a large vocabulary of technical terms. We trained an NMT system on a bilingual corpus, wherein technical terms are replaced with technical term tokens; this allows it to translate most of the source sentences except the technical terms. Similar to Sutskever et al. (2014), we used it as a decoder to translate the source sentences with technical term tokens and replace the tokens with technical terms translated using SMT. We also used it to rerank the 1,000-best SMT translations on the basis of the average of the SMT score and that of NMT rescoring of translated sentences with technical term tokens. For the translation of Japanese patent sentences, we observed that our proposed NMT system performs better than the phrase-based SMT system as well as the equivalent NMT system without our proposed approach. One of our important future works is to evaluate our proposed method in the NMT system proposed 55
Page 1 and 2:
WAT 2016 The 3rd Workshop on Asian
Page 3:
Preface Many Asian countries are ra
Page 6 and 7:
Technical Collaborators Luis Fernan
Page 9 and 10:
Table of Contents Overview of the 3
Page 11 and 12:
Conference Program December 12, 201
Page 13 and 14:
December 12, 2016 (continued) 12:00
Page 15 and 16:
Overview of the 3rd Workshop on Asi
Page 17 and 18: LangPair Train Dev DevTest Test JPC
Page 19 and 20: ASPEC JPC IITB BPPT pivot System ID
Page 21 and 22: 3.6 Tree-to-String Syntax-based SMT
Page 23 and 24: • Use of other resources in addit
Page 25 and 26: ASPEC JPC BPPT IITBC pivot Team ID
Page 27 and 28: ut the adequacy is worse. As for AS
Page 29 and 30: Figure 3: Official evaluation resul
Page 37 and 38: Figure 11: Official evaluation resu
Page 39 and 40: Figure 13: Official evaluation resu
Page 41 and 42: Annotator A Annotator B all weighte
Page 43 and 44: Kyoto-U 2 bjtu nlp UT-KAY 1 UT-KAY
Page 45 and 46: Online B IITP-MT EHR Online A ≫
Page 47 and 48: SYSTEM ID ID METHOD OTHER RESOURCES
Page 49 and 50: SYSTEM ID ID METHOD OTHER BLEU RIBE
Page 59 and 60: Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
Page 61 and 62: Translation of Patent Sentences wit
Page 63 and 64: Chinese sentences contain fewer tha
Page 65 and 66: Figure 3: NMT decoding with technic
Page 67: Table 1: Automatic evaluation resul
Page 71 and 72: K. Papineni, S. Roukos, T. Ward, an
Page 73 and 74: Table 1: Examples of title, ingredi
Page 75 and 76: text. In this section, we explain t
Page 77 and 78: Table 3: Automatic evaluation BLEU/
Page 79 and 80: Table 5: Number of fluency errors i
Page 81 and 82: Kenneth Heafield. 2011. Kenlm: Fast
Page 83 and 84: under-studied. MorphInd, a morphana
Page 85 and 86: dimensions of 150 units in second h
Page 87 and 88: distribution as received in JPO ade
Page 89 and 90: Domain Adaptation and Attention-Bas
Page 91 and 92: Once s M , which represents the ent
Page 93 and 94: 3.4.2 Ensemble of Attention Scores
Page 95 and 96: Type Count Ratio (A) Correct 76 30.
Page 97 and 98: Minh-Thang Luong, Hieu Pham, and Ch
Page 99 and 100: Pair 1 Japanese [[ A ][ B ][ C ]
Page 101 and 102: ID n-grams len freq m1 prevent, | 1
Page 103 and 104: prediction accuracy with respect to
Page 105 and 106: Reference: [ A To provide] [ B a to
Page 107 and 108: Philipp Koehn. 2004. Statistical si
Page 109 and 110: Therefore, we propose to use phrase
Page 111 and 112: Figure 2: An NRM considering phrase
Page 113 and 114: Mono Swap D right D left Acc. The r
Page 115 and 116: Method ja-en en-ja BLEU RIBES WER B
Page 117 and 118: Peng Li, Yang Liu, and Maosong Sun.
Page 119 and 120:
Figure 1: The framework of NMT. Whe
Page 121 and 122:
For long sentence, we discarded all
Page 123 and 124:
As shown in the above tables and fi
Page 125 and 126:
Translation systems and experimenta
Page 127 and 128:
The number of TM training sentence
Page 129 and 130:
where, f, p and e means a source, p
Page 131 and 132:
former system can translate more th
Page 133 and 134:
Lexicons and Minimum Risk Training
Page 135 and 136:
2.2 Parameter Optimization If we de
Page 137 and 138:
ML (λ=0.0) ML (λ=0.8) MR (λ=0.0)
Page 139 and 140:
Graham Neubig. 2013. Travatar: A fo
Page 141 and 142:
Feature Space Common (h ) Domain 1
Page 143 and 144:
Preprocessing Training Translation
Page 145 and 146:
JPC ASPEC LM Method Ja-En En-Ja Ja-
Page 147 and 148:
Translation Using JAPIO Patent Corp
Page 149 and 150:
4 Results Table 1 shows official ev
Page 151 and 152:
Table 3: Examples of translation er
Page 153 and 154:
An Efficient and Effective Online S
Page 155 and 156:
#$ #%$ #$ !""
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
Page 167 and 168:
-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3
Page 169 and 170:
1.800 1.600 1.400 1.200 1.000 0.800
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
Page 177 and 178:
the decoded sentence and the refere
Page 179 and 180:
Kenneth Heafield. 2011. Kenlm: Fast
Page 181 and 182:
Figure 1: Overview of KyotoEBMT. Th
Page 183 and 184:
proportionally slower to compute (a
Page 185 and 186:
3.6 Ensembling Ensembling has previ
Page 187 and 188:
when Chinese is also the language m
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
Page 197 and 198:
Razvan Pascanu, Tomas Mikolov, and
Page 199 and 200:
governs the grammaticality of the t
Page 201 and 202:
2.1 Quantization and Binarization o
Page 203 and 204:
3 Results Team Other Resources Syst
Page 205 and 206:
Peter F Brown, John Cocke, Stephen
Page 207 and 208:
Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210:
Language Sentence Chinese 该 / 钽
Page 211 and 212:
Chinese or Japanese sentences Chine
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
Page 219 and 220:
Figure 2: Flow of the voice predict
Page 221 and 222:
ecause the number of passive senten
Page 223 and 224:
controlling the sentence length wit
Page 225 and 226:
Chinese-to-Japanese Patent Machine
Page 227 and 228:
using the reordering oracles over t
Page 229 and 230:
Conference on Natural Language Proc
Page 231 and 232:
Figure 1: Hierarchical approach wit
Page 233 and 234:
newswire test and development set o
Page 235 and 236:
Source: the rain and cold wind on W
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
show all

December 11-16 2016 Osaka Japan

Create successful ePaper yourself

Delete template?

Save as template?