December 11-16 2016 Osaka Japan

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NMT embeds each source word into a d-dimensional vector and generates a target sentence from the vectors (Sutskever et al., 2014). Even though the method does not use any syntactic information, it can generate grammatical sentences. However, due to the execution time it requires, NMT generally limits the size of the vocabulary for a target language. Therefore, compared with PBSMT, which can handle many phrases in the target language, NMT has a disadvantage in that it cannot generate low frequent words. The method also has the disadvantage that it often generates target words that do not correspond to any words in the source sentences (Tu et al., 2016). The setting for each method in this study was as follows. We used the parallel corpus described in Section 2 as our corpus, Moses (ver.2.1.1) (Koehn et al., 2007) as the PBSMT method, and conducted Japanese word segmentation using MeCab (Kudo et al., 2004) with IPADIC (ver.2.7.0) as the dictionary. Word alignment was obtained by running Giza++. The language model was learned with the English side of the recipe corpus using KenLM (Heafield, 2011) with 5-gram. Other resources in English were not used for training the language model because the style of recipe texts is different from general corpus in that it contains many noun phrases in title and ingredient, and many imperatives in step. The size of the phrase table was approximately 3 million pairs, and we used the development set to tune the weights for all features by minimum error rate training (MERT) (Och, 2003). We used the default parameter 6 for the distortion limit. We reimplemented the NMT model in accordance with (Bahdanau et al., 2015). Note that we used long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997) instead of gated recurrent unit (Cho et al., 2014) for each recurrent neural network (RNN) unit of the model. The model had 512-dimensional word embeddings and 512-dimensional hidden units with one layer LSTM. We set the vocabulary size of the model to 30, 000, and we did not perform any unknown word processing during training. Adagrad (Duchi et al., 2011) was used with the initial learning rate of 0.01 as an optimization method. The initial values for word embeddings on both sides were obtained by training word2vec 2 with default setting because better results were shown in our preliminary experiments. The initial word embeddings on the source side were learned with a raw Japanese recipe corpus (Harashima et al., 2016) consisting of approximately 13 million step sentences. Conversely, initial word embeddings on the target side were learned with approximately 120, 000 English step sentences included in the parallel corpus. Title sentences were not used for learning because they were often written with free expression largely different depending on each recipe. Ingredient sentences were also not used because most of them consisted of a few words. The batch size was set to 64 and the number of epochs was set to 10. We selected the model that gave the highest BLEU score in the development set for testing. Beam search for decoding in NMT was not carried out. When testing, the output length was set up to 40 words. Each output was evaluated via two metrics: bilingual evaluation understudy (BLEU) (Papineni et al., 2002) score and rank-based intuitive bilingual evaluation score (RIBES) (Isozaki et al., 2010). BLEU is more sensitive to word agreement than RIBES, whereas RIBES is more sensitive to word order evaluation. We set two hyper-parameters for RIBES: α was 0.25 and β was 0.10. 4 Error Classification of Recipe Translation We conducted blackbox analysis on the outputs of PBSMT and NMT. Blackbox analysis is a type of analysis that does not take into account how translation output is obtained. The error classification used for this analysis is based on the MQM ANNOTATION DECISION TREE (Burchardt and Lommel, 2014) because it makes the classification of each error more consistent. The method classifies each error by following a decision tree where each node asks a Yes/No question. If the question is answered with a ‘Yes’, the corresponding text span is classified as the error specified by the tree node. When a text span is classified as an error at higher priority, that part is not classified as other errors. The same process continues until the lowest priority error is checked. The error classification defined in MQM is roughly divided into two parts: Accuracy and Fluency. Accuracy addresses the extent to which the target text accurately renders the meaning of the source text. It is usually called ‘Adequacy’ in the literature. Fluency relates to the monolingual qualities of the target 2 https://radimrehurek.com/gensim/models/word2vec.html 60
text. In this section, we explain the fine classification of accuracy and fluency metrics in detail and describe how to classify and analyze the errors. 4.1 Accuracy In terms of accuracy, MQM defines (1) Omission, (2) Untranslated, (3) Addition, (4) Terminology, (5) Mistranslation and (6) General. In this study, we adapted the MQM ANNOTATION DECISION TREE to recipe translation to classify each error. We modified the original MQM ANNOTATION DECISION TREE in three different ways. First, we divided mistranslation errors into substitution and word order and considered them independently. This is because the tendency of substitution and word order is so different in a distant language pair such as Japanese and English that the difference should be reflected. Second, we defined a new order to classify each error, in which substitution and word order are given the highest priority. This makes the classification of substitution easy, especially for NMT, which sometimes outputs completely different target words from source sentences. Third, we excluded terminology because terminology-related errors do not occur when only a single domain, such as food recipes, is considered. Therefore, in this study, the following fine classification was applied: (1) Substitution, (2) Word order, (3) Omission, (4) Untranslated, (5) Addition, (6) General. Here, we explain the definition of each error classification with examples. Accuracy (Substitution) The target content does not represent the source content owing to inappropriate words. In the following example, ‘Heat’ is used for ‘’ (break). (2) egg ACC break . Heat an egg . Accuracy (Word order) The target content does not represent the source content owing to inappropriate word positions. In the following example, ‘from step 1’ should be placed after ‘into a bowl .’. (3) 1 1 from bowl into lettuce ACC add . Add the lettuce from step 1 into a bowl . Accuracy (Omission) Content in the source sentence is missing from the translation. In the following example, the translation does not contain a word for ‘’ (honey). (4) 1 honey dough ACC first fermenatation until finish . Make the dough until the first rising . Accuracy (Untranslated) Source words have been left untranslated. From the following example, it can be seen that there is an untranslated word ‘’ (narrow). (5) , length ACC adjust , width NOM narrow with cut . Adjust the length , and cut the into it . Accuracy (Addition) The translation includes words or phrases that are not present in the source sentence. In the following example, ‘red’ and ‘into a pot’ should not have been added. (6) sauce ACC add . Add the red sauce into a pot . 61
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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
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 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: Table 1: Examples of title, ingredi
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
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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
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Lexicons and Minimum Risk Training
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2.2 Parameter Optimization If we de
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ML (λ=0.0) ML (λ=0.8) MR (λ=0.0)
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Graham Neubig. 2013. Travatar: A fo
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Feature Space Common (h ) Domain 1
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Preprocessing Training Translation
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JPC ASPEC LM Method Ja-En En-Ja Ja-
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Translation Using JAPIO Patent Corp
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4 Results Table 1 shows official ev
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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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