December 11-16 2016 Osaka Japan

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Table 1: Evaluation of Japanese-to-English translation where glob-pre denotes global pre-ordering and pre denotes conventional syntactic pre-ordering, dl denotes distortion limit, HPB denotes hierarchical phrase-based SMT and TDBTG denotes reordering based on top-down bracketing transduction grammar. The bold numbers indicate a statistically insignificant difference from the best system performance according to the bootstrap resampling method at p = 0.05 (Koehn, 2004). Reordering config Settings Results glob-pre pre SMT glob-pre pre RIBES BLEU T1 T2 T3 T4 PB dl=6 44.9 17.9 PB dl=20 53.7 (+8.8) 21.3 (+3.4) HPB 54.9 (+10.0) 23.1 (+5.2) √ PB dl=6 heuristic 61.7 (+16.8) 19.6 (+1.7) PB dl=6 SVM 61.0 (+16.1) 19.3 (+1.4) √ PB dl=6 TDBTG 64.6 (+19.7) 22.3 (+4.4) PB dl=6 syntactic 64.9 (+20.0) 25.5 (+7.6) √ √ PB dl=6 heuristic syntactic 71.3 (+26.4) 25.3 (+7.4) PB dl=6 SVM syntactic 72.1 (+27.2) 25.6 (+7.7) T1 T2 T3 T4 Table 2: Evaluation of English-to-Japanese translation Reordering config Settings Results glob-pre pre SMT glob-pre pre RIBES BLEU PB dl=6 43.2 27.9 PB dl=20 54.4 (+11.1) 29.0 (+1.1) HPB 59.1 (+15.8) 32.1 (+4.2) √ PB dl=6 heuristic 59.5 (+16.2) 28.4 (+0.5) PB dl=6 SVM 65.3 (+22.1) 29.1 (+1.2) √ PB dl=6 TDBTG 77.7 (+34.5) 34.9 (+7.0) PB dl=6 syntactic 76.1 (+32.8) 36.9 (+9.0) √ √ PB dl=6 heuristic syntactic 77.3 (+34.1) 36.5 (+8.6) PB dl=6 SVM syntactic 77.7 (+34.5) 36.5 (+8.6) junction with conventional pre-ordering. Also, the combination of global syntactic pre-ordering performs significantly better than syntactic pre-ordering alone. The BLEU score is not as sensitive to the introduction of global reordering, probably because the improvement is mainly concerned with long-distance reordering. We will further discuss the matter of evaluation metrics in the following section. Figure 6 shows typical translations of the four reordering configurations: T1, T2, T3 and T4. Compared with the reference, the baseline (T1) fails to produce segment A and fails to output segments B and C in the correct order. In addition, the word order within each segment is not appropriate. The baseline with global pre-ordering (T2) successfully produces all three segments in the correct order, although the quality within each segment is not improved. The translation using conventional pre-ordering alone (T3) improves the local word order, while it fails to arrange the segments in the correct order. The translation with global and syntactic pre-ordering (T4) successfully produces the segments in the correct order, while at the same time improving the word order in each of the segments. 6 Analysis To evaluate the ability of our proposed method to produce appropriate sentence structures in translated sentences, we count the number of sentences with correct global sentence structures among the translated test sentences. We think this is an important figure because the failure to produce the correct global sentence structure leads to inappropriate translation in most sublanguage-specific translations. We consider 90
Reference: [ A To provide] [ B a toner cake layer forming apparatus] [ C which forms a toner cake layer having a high solid content and which can be actuated by an electrostatic printing engine.] T1: [ C Solid content of high toner cake layer for generating an electrostatic print engine operates in] [ B a toner cake layer forming device.] T2: [ A To provide] [ B toner cake layer forming apparatus] [ C of the solid content of high toner cake layer for generating an electrostatic print engine can be operated.] T3: [ C For generating toner cake layer having a high solids content and] [ A to provide] [ B a toner cake layer forming device] [ C which can be operated by an electrostatic printing engine.] T4: [ A To provide] [ B a toner cake layer forming device] [ C for generating toner cake layer having a high solid content, and operable by an electrostatic printing engine.] Figure 6: Typical translations T1 T2 T3 T4 Table 3: Number of correct global reordering in 100 test sentences Correct global reordering Reordering config Settings in 100 test sentences glob-pre pre SMT glob-pre pre Japanese-to English English-to Japanese PB dl=6 4 6 PB dl=20 12 15 HPB 11 28 √ PB dl=6 heuristic 24 23 PB dl=6 SVM 31 26 √ PB dl=6 TDBTG 21 59 PB dl=6 syntactic 27 67 √ √ PB dl=6 heuristic syntactic 46 68 PB dl=6 SVM syntactic 58 63 that a translated sentence has a correct global sentence structure if it meets the following two criteria: (a) The translated sentence actually has the sentence structure CBA, where the source sentence structure ABC must be reordered to CBA in the target sentence. All the segments must be present in the correct order in the translated sentence; (b) All the words in each of the ABC segments in the source sentence must appear in an undivided segment in the target sentence. We randomly select a portion of the translated test sentences and manually counted the number of sentences meeting these criteria. Table 3 shows the number of correct global reordering in Japanese-to-English and English-to-Japanese translations out of the 100 sentences randomly selected from the test set. The table shows that T4, which combines global and syntactic reordering, has a largely improved sentence structure compared with T1 and T2. In case of Japanese-to-English translation, the performance of T4 is much higher than T3, the state-of-the-art reordering alone. In case of an English-to-Japanese translation, the performance of the syntactic reordering is already considerably higher than the baseline and hence the performance of T4 is comparable to that of T3. The prominent improvement in BLEU scores by HPB observed in Tables 1 and 2 do not appear as prominent in Table 3, probably because HPB deals with more local reordering which is reflected well by BLEU score, but does not contribute much to global reordering. 7 Conclusion In this paper, we proposed a global pre-ordering method that supplements conventional syntactic preordering and improves translation quality for sublanguages. The proposed method learns global reordering models without syntactic parsing from a non-annotated corpus. Our experimental results on the patent abstract sublanguage show substantial gains of more than 25 points in RIBES and comparable BLEU scores for Japanese-to-English and English-to-Japanese translations. 91
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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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SYSTEM ID ID METHOD OTHER RESOURCES
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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 and 102: ID n-grams len freq m1 prevent, | 1
Page 103: prediction accuracy with respect 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
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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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