December 11-16 2016 Osaka Japan

More documents

Recommendations

Info

JPC Method Ja-En En-Ja Ja-Zh Zh-Ja Single-Domain Model 34.58 38.06 33.35 39.54 Corpus Concatenation 35.64 38.61 34.27 40.96 Domain Adaptation 35.68 39.03 34.64 41.09 Table 3: BLEU Scores on JPO Corpus (official scores) JPC Method Ja-En En-Ja Ja-Zh Zh-Ja Single-Domain Model 35.12(-) 37.40(-) 31.96(-) 38.15(-) Corpus Concatenation 36.22 38.03(-) 32.92(-) 39.68(-) Domain Adaptation 36.29 38.48 33.36 39.85 Table 4: BLEU Scores on JPO Corpus (MultEval scores) 4 Experimental Results For evaluation, we used two toolkits based on BLEU (Papineni et al., 2002). One is the official BLEU scores provided by the WAT2016 committee. Because the official tool cannot measure a significance level of two systems, we also used the MultEval tool (Clark et al., 2011), which can measure significance levels based on bootstrap resampling. Since we represent the mean scores of three optimizations, the MultEval scores differ from the official scores. 4.1 JPO Corpus (without External Knowledge) For JPO corpus experiments, we did not use external knowledge and compared translations of the singledomain model, corpus concatenation, and domain adaptation. The JPO corpus was divided into four domains (chemistry, electricity, machine, and physics). Tables 3 and 4 show the results evaluated by the official scorer and MultEval tools, respectively. The symbol (-) indicates that the score was significantly degraded compared to that of the domain adaptation (p < 0.05). Note that test sentences of each domain were translated using the corresponding models, and the BLEU score was computed by concatenating all test sentences as a document. Results are presented in Table 4. Corpus concatenation corresponds to typical translation quality where only the JPO corpus was used. The single-domain model scores were inferior to the corpus concatenation scores because the corpus sizes were reduced by one-quarter. In contrast, the domain adaptation scores for most language pairs improved significantly and the domain adaptation was successful. 4.2 JPO and ASPEC Corpora (with External Knowledge) Next, we conducted experiments using five domains with the JPO and ASPEC corpora. In these experiments, we evaluated the effects of external knowledge using the Google n-gram language model. The results are shown in Tables 5 and 6. We first describe the effects of external knowledge, as shown in Table 6. In Table 6, the upper and lower halves show the BLEU scores before and after adding the Google n-gram language model, respectively. By adding the Google n-gram LMs, 0.27, 0.82, and 0.12 BLEU scores were improved on average in the JPO domains of Ja-En, En-Ja and Zh-Ja pairs, respectively. In the ASPEC domain, −0.03, 0.56, and 0.67 BLEU scores were improved. Except for the Ja-En pair of the ASPEC domain, the Google n-gram language model contributed to translation quality. The Japanese model tends to be suitable for JPO and ASPEC domains compared to the English model. Next, we focused on the effect of domain adaptation with the Google n-gram LMs. In most cases, domain adaptation worked effectively except for the Ja-En pair of the ASPEC domain because the BLEU scores improved or were maintained the same level compared to those of the single-domain model and 130
JPC ASPEC LM Method Ja-En En-Ja Ja-Zh Zh-Ja Ja-En En-Ja Ja-Zh Zh-Ja w/o Single-Domain Model 33.67 38.75 33.27 40.06 21.54 33.97 30.12 39.33 GN Corpus Concatenation 35.49 39.18 33.94 41.08 20.90 33.11 29.66 37.84 Domain Adaptation 35.96 40.14 34.64 41.93 21.34 34.21 29.97 39.51 w/ Single-Domain Model 33.99 39.63 40.47 21.64 34.59 40.01 GN Corpus Concatenation 35.73 40.23 41.31 20.80 33.78 38.30 Domain Adaptation 36.06 40.90 41.87 21.54 34.67 40.02 Table 5: BLEU Scores on JPO and ASPEC Corpora (official scores) JPC ASPEC LM Method Ja-En En-Ja Ja-Zh Zh-Ja Ja-En En-Ja Ja-Zh Zh-Ja w/o Single-Domain Model 33.90(-) 38.19(-) 31.78(-) 38.74(-) 22.79 34.80 29.47(+) 38.96(-) GN Corpus Concatenation 35.81(-) 38.62(-) 32.76(-) 39.96(-) 22.20(-) 33.94(-) 28.95(-) 37.62(-) Domain Adaptation 36.25 39.58 33.53 40.76 22.80 34.91 29.28 39.18 w/ Single-Domain Model 34.35(-) 39.04(-) 38.90(-) 22.87(+) 35.42 39.74(-) GN Corpus Concatenation 36.03(-) 39.48(-) 40.14(-) 22.10(-) 34.55(-) 38.15(-) Domain Adaptation 36.40 40.32 40.77 22.74 35.36 39.87 Table 6: BLEU Scores on JPO and ASPEC Corpora (MultEval scores) corpus concatenation. However, we confirmed that the effects of the ASPEC domain were less than those of the JPO domains because the score did not improve significantly. This is because the ASPEC domain uses one million bilingual sentences; thus, domain adaptation could not contribute to the high-resource domains. 5 Conclusions We have described the NICT-2 translation system. The proposed system employs Imamura and Sumita (2016)’s domain adaptation. In this study, we regarded the JPO corpus as a mixture of four domains and improved the translation quality. Although we added the ASPEC corpus as a fifth domain, the effects were not significant. Our domain adaptation can incorporate external knowledge, such as Google n-gram language models. The proposed domain adaptation can be applied to existing translation systems with little modification. Acknowledgments This work was supported by “Promotion of Global Communications Plan — Research and Development and Social Demonstration of Multilingual Speech Translation Technology,” a program of the Ministry of Internal Affairs and Communications, Japan. References Colin Cherry and George Foster. 2012. Batch tuning strategies for statistical machine translation. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 427–436, Montréal, Canada, June. Jonathan H. Clark, Chris Dyer, Alon Lavie, and Noah A. Smith. 2011. Better hypothesis testing for statistical machine translation: Controlling for optimizer instability. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 176–181, Portland, Oregon, USA, June. Hal Daumé, III. 2007. Frustratingly easy domain adaptation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 256–263, Prague, Czech Republic, June. 131
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 31 and 32:
Page 33 and 34:
Page 35 and 36:
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 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
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 and 68:
Table 1: Automatic evaluation resul
Page 69 and 70:
Figure 6: Example of correct transl
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: Preprocessing Training Translation
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?