December 11-16 2016 Osaka Japan

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96.5 97.5 Accuracy [%] 96.0 95.5 95.0 Size of reordering corpus 100k 10k 1k 100 Accuracy [%] 97.0 96.5 96.0 95.5 95.0 Size of reordering corpus 100k 10k 1k 100 94.5 0 5 10 15 Order of n-grams 94.5 0 5 10 15 Order of n-grams Figure 4: Variation in the boundary prediction accuracy for Japanese input Figure 5: Variation in the boundary prediction accuracy for English input • N-grams: Here, n-grams are extracted from both sides of the word under training/prediction. In contrast to the heuristics-based method, for simplicity, we use here the same value of n for n-grams in the left- and right-hand sides of the examined word. The n-grams used are as follows, where f is the sentence, i is the index of the word in question, and n is the value n of n-grams. (f i−n+1 , f i−n+2 · · · f i · · · f i+n−1 , f i+n ) (4) • Position in the sentence: The position of the word under training/prediction is provided as a feature. This feature is introduced to differentiate multiple occurrences of identical n-grams within the same sentence. The position value is calculated as the position of the word counted from the beginning of the sentence divided by the number of words contained in the sentence. This is shown as follows, where i denotes the index of the word in question and F is the number of words contained in the sentence. i (5) F In the prediction process, we extract the features corresponding to the word position i and then input these features to the SVM model to make a prediction for i. By repeating this prediction process for every i in the sentence, we obtain a sentence with each position i marked either as a segment boundary or as not a segment boundary. These predicted segments are then reordered globally to produce the global sentence structure of the target language. 4 Experiments In this section, we first describe the reordering configuration for depicting the effect of global preordering. We then describe the primary preparation of global reordering, followed by a description of the settings used in our translation experiment. 4.1 Reordering Configuration To illustrate the effect of introducing global pre-ordering, we evaluate the following four reordering configurations: (T1) Baseline SMT without any reordering; (T2) T1 with global pre-ordering only. The input sentence is globally pre-ordered, and this reordered sentence is translated and evaluated; (T3) T1 with conventional syntactic pre-ordering (Goto et al., 2015). The input sentence is pre-ordered using conventional syntactic pre-ordering and the reordered sentence is translated and evaluated; and (T4) T1 with a combination of syntactic and global pre-ordering. The input sentence is globally pre-ordered, each segment is reordered using syntactic pre-ordering and the reordered sentence is translated and evaluated. 4.2 Preparation of Global Reordering In preparation for global pre-ordering, we calibrated the machine learning-based detection to determine the optimal feature set for detecting segments. To determine the optimal feature set, we plotted the 88
prediction accuracy with respect to the size of the global reordering corpus and value n of n-grams. As our support vector machines, we used liblinear 1.94 (Fan et al., 2008) for training and prediction. Figure 4 shows the variation in the prediction accuracy with respect to the size of the global reordering corpus and the order of an n-gram for Japanese input. Figure 5 shows the same for English input. The accuracy is the average accuracy of a ten-fold cross-validation for the global reordering corpus. From the calibration shown in the tables, we select the settings producing the highest prediction accuracy, namely, a value of five for the n of n-grams and a size of 100k for the global reordering corpus, for both Japanese and English inputs. 4.3 Translation Experiment Setup Data As our experimental data, we use the Patent Abstracts of Japan (PAJ), the English translations of Japanese patent abstracts. We automatically align (Utiyama and Isahara, 2007) PAJ with the corresponding original Japanese abstracts, from which we randomly select 1,000,000 sentence pairs for training, 1,000 for development and 1,000 for testing. This training data for the translation experiment are also used for training global reordering as described in the previous subsection. Out of the 1,000 sentences in the test set, we extract the sentences that show any matching with the n-grams and use these sentences for our evaluation. In our experiments, the number of sentences actually used for evaluation is 300. Baseline SMT The baseline system for our experiment is Moses phrase-based SMT (Koehn et al., 2007) with the default distortion limit of six. We use KenLM (Heafield et al., 2013) for training language models and SyMGIZA++ (Junczys-Dowmunt and Szal, 2010) for word alignment. The weights of the models are tuned with the n-best batch MIRA (Cherry and Foster, 2012). As variants of the baseline, we also evaluate the translation output of the Moses phrase-based SMT with a distortion limit of 20, as well as that of the Moses hierarchical phrase-based (Chiang, 2005) SMT with the default maximum chart span of ten. Conventional syntactic pre-ordering Syntactic pre-ordering is implemented on the Berkeley Parser. The input sentences are parsed using the Berkeley Parser, and the binary nodes are swapped by the classifier (Goto et al., 2015). As a variant of conventional reordering, we also use a reordering model based on the top-down bracketing transducer grammar (TDBTG) (Nakagawa, 2015). We use the output of mkcls and SyMGIZA++ obtained during the preparation of the baseline SMT for training TDBTG-based reordering. Global pre-ordering Global pre-ordering consists of the detection of segment boundaries and the reordering of the detected segments. Out of the 1,000,000 phrase-aligned sentence pairs in the training set for SMT, we use the first 100,000 sentence pairs for extracting the sentence pairs containing global reordering. We only use a portion of the SMT training data due to the slow execution speed of the current implementation of the software program for extracting sentence pairs containing global reordering. We evaluate both the heuristic and the machine learning-based methods for comparison. Evaluation metrics We use the RIBES (Isozaki et al., 2010a) and the BLEU (Papineni et al., 2002) scores as evaluation metrics. We use both metrics because n-gram-based metrics such as BLEU alone cannot fully illustrate the effects of global reordering. RIBES is an evaluation metric based on rank correlation which measures long-range relationships and is reported to show much higher correlation with human evaluation than BLEU for evaluating document translations between distant languages (Isozaki and Kouchi, 2015). 5 Results The evaluation results based on the present translation experiment are shown in Tables 1 and 2 for Japanese-to-English and English-to-Japanese translations respectively, listing the RIBES and BLEU scores computed for each of the four reordering configurations. The numbers in the brackets refer to the improvement over the baseline phrase-based SMT with a distortion limit of six. A substantial gain of more than 25 points in the RIBES scores compared to the baseline is observed for both Japanese-to-English and English-to-Japanese translations, when global pre-ordering is used in con- 89
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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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Page 53 and 54: SYSTEM ID ID METHOD OTHER BLEU RIBE
Page 55 and 56: SYSTEM ID ID METHOD OTHER RESOURCES
Page 57 and 58: 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 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: ID n-grams len freq m1 prevent, | 1
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
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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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Similar Southeast Asian Languages:
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τ around 0.71, and the Malay-Indon
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-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3
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1.800 1.600 1.400 1.200 1.000 0.800
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Integrating empty category detectio
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3 Preordering with empty categories
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We performed the tests under two co
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the decoded sentence and the refere
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Kenneth Heafield. 2011. Kenlm: Fast
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Figure 1: Overview of KyotoEBMT. Th
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proportionally slower to compute (a
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3.6 Ensembling Ensembling has previ
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when Chinese is also the language m
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Character-based Decoding in Tree-to
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where W s ∈ R d×d is a matrix an
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Model BLEU (BP) RIBES Character-bas
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The word-based NMT models cannot us
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Razvan Pascanu, Tomas Mikolov, and
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governs the grammaticality of the t
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2.1 Quantization and Binarization o
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3 Results Team Other Resources Syst
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Peter F Brown, John Cocke, Stephen
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Yonghui Wu, Mike Schuster, Zhifeng
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Language Sentence Chinese 该 / 钽
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Chinese or Japanese sentences Chine
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4 Experiments and Results 4.1 Chine
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6 Conclusion and Future Work In thi
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Controlling the Voice of a Sentence
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Figure 2: Flow of the voice predict
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ecause the number of passive senten
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controlling the sentence length wit
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Chinese-to-Japanese Patent Machine
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using the reordering oracles over t
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Conference on Natural Language Proc
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Figure 1: Hierarchical approach wit
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newswire test and development set o
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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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