December 11-16 2016 Osaka Japan

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the noise, and is not directly linked to an increased robustness to prediction errors. We should do further experiments to verify if there is a marked difference in results between adding noise the way we do, and simply adding a dropout layer to any part of the network. 3.5 Beam Search The naive way to translate using an NMT system such as the one we described is, after feeding the input sentence, to produce the target words with the highest probability at each step. This is however sub-optimal, as a better translation might include a sub-sequence that is locally less likely than another. Hence, the idea is to use a form of beam-search to keep track of several translation hypotheses at once. There are a few differences in the way we handle beam search, compared with other NMT implementations such as the one originally provided by the LISA lab of Université de Montréal. 6 We came to this method after a few iterative trials. We detail our beam search procedure in Algorithm 1. Given an input sentence i of length L i , we first estimate the maximum length of the translation L mt . L mt is estimated by L mt = r · L i , where r is a language dependent ratio. We empirically found the following values to work well : r = 1.2 for Japanese-to-English, r = 2 for English-to-Japanese, and r = 1.5 for Japanese-to-Chinese and Chinese-to-Japanese. At the end, we found it beneficial to rank the generated translations by their log-probability divided by their length, instead of simply using the log-probability. This helps to counter-balance the fact that the model will otherwise tend to be biased towards shorter translations. One could fear that doing this will actually bias the model towards longer translations, but we did not observe such a thing; maybe in part due to the fact that our algorithm caps the maximum length of a translation through the languagedependent length ratio. Although we have seen some claims that large beam width was not very helpful for NMT decoding, we actually verified empirically that using a beam-width of 100 could give significantly better results than a beam-width of 30 or less (of course, at the cost of a serious slow-down in the translation speed). We used a beam-width of 100 in all our submitted results. Algorithm 1 Beam Search 1: Input: decoder dec conditionalized on input sentence i, beam width B 2: L mt ← r · |i| ▷ L mt : Maximum translation length, r: Language-dependent length ratio 3: finished ← [] ▷ list of finished translations (log-prob, translation) 4: beam ← array of L mt item lists ▷ an item: (log-probability, decoder state, partial translation) 5: beam[0] ← [(0, st i , ”)] ▷ st i : initial decoder state 6: for n ← 1 to L mt do 7: for (lp, st, t) ∈ beam[n − 1] do 8: prob, st ′ ← dec(st, t[−1]) ▷ dec return the probability of next words, and the next state 9: for w, p w ∈ top B (prob) do ▷ top B return the B words with highest probability 10: if w = EOS then 11: add (lp + log(p w ), t) to finished 12: else 13: add (lp + log(p w ), st ′ , t + w) to beam[n] 14: end if 15: end for 16: end for 17: prune beam[n] 18: end for 19: Sort (lp, t) ∈ finished according to lp/|t| 20: return t s.t. lp/|t| is maximum 6 https://github.com/lisa-groundhog/ 170
3.6 Ensembling Ensembling has previously been found to widely increase translation quality of NMT systems. Ensembling essentially means that, at each translation step, we predict the next word using several NMT models instead of one. The two “classic” ways of combining the prediction of different systems are to either take the geometric average or the arithmetic average of their predicted probabilities. Interestingly, although it seems other researchers have reported that using the arithmetic average works better (Luong et al., 2016), we actually found that geometric average was giving better results for us. Ensembling usually works best with independently trained parameters. We actually found that even using parameters from a single run could improve results. This had also been previously observed by (Sennrich et al., 2016). Therefore, for the cases when we could only run one training session, we ensembled the three parameters corresponding to the best development loss, the best development BLEU, and the final parameters (obtained after continuing training for some time after the best development BLEU was found). We refer to this as “self-ensembling” in the result section. When we could do n independent training, we kept these three parameters for each of the independent run and ensembled the 3 · n parameters. 3.7 Preprocessing The preprocessing steps were essentially the same as for KyotoEBMT. We lowercased the English sentences, and segmented automatically the Japanese and Chinese sentences. We used JUMAN to segment the Japanese and SKP to segment the Chinese. We also tried to apply BPE segmentation (Sennrich et al., 2015) in some cases. 7 4 Results We submitted the translation results of both EBMT and NMT systems, however, the automatic evaluation results of NMT seemed to be quite superior to those of EBMT. Therefore, we selected NMT results for the human evaluations for almost all the subtasks. 4.1 Specifics NMT Settings for Each Language Pair The NMT training is quite slow. On an Nvidia Titan X (Maxwell), we typically needed 4∼7 days of training for ASPEC-CJ, and more than 2 weeks for ASPEC-EJ. Therefore, it took us a lot of time to experiment with different variations of settings, and we did not have the time to fully re-run experiments with the best settings. We describe here the settings of the results we submitted for human evaluation. Ja → En Submission 1 corresponds to only one trained model, with self-ensembing (see Section 3.6). In this case, source vocabulary was reduced to 200k words. For target vocabulary, we reduced the vocabulary to 52k using BPE. We used a double-layer LSTM. Submission 2 is an ensemble of four independently trained models, two of which were using GRUs, and two of which were using LSTM. Source and target vocabularies were restricted to 30k words. For each model, we actually used three sets of parameters (best development BLEU, best development perplexity, final), as described in Section 3.6. Therefore, ensembling was actually using 12 different models. En → Ja Submission 1 also corresponds to only one trained model. BPE was applied to both source and target sides to reduce vocabulary to 52k. We used a double-layer LSTM and self-ensembling. Ja → Zh Submission 1 corresponds to one trained model with self-ensembling, with double-layer LSTM and 30k words for both source and target vocabularies. Zh → Ja Submission 1 corresponds to ensembling two independently trained models using doublelayer LSTM with 30k source and target vocabularies. In addition, a model was trained on reversed Japanese sentences and was used to rescore the translations. Submission 2 corresponds to one independently trained model, using self-ensembling and using 200k words for source vocabulary and 50k words for target vocabulary. 7 using the BPE segmentation code at https://github.com/rsennrich/subword-nmt 171
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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
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
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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: proportionally slower to compute (a
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
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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
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Source: the rain and cold wind on W
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Residual Stacking of RNNs for Neura
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2.2 Generalized Minimum Bayes Risk
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5.0 4.5 4.0 Baseline model Enlarged
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Felix Hill, Kyunghyun Cho, Sebastie
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December 11-16 2016 Osaka Japan

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