A Chinese-Japanese Lexical Machine Translation through a Pivot Language

Tsunakawa, Takashi; Okazaki, Naoaki; Xiao, Liangang; Tsujii, Jun’ichi

doi:10.1145/1526252.1526257

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Tsunakawa et al [31] proposed an integrated framework for building a bilingual lexicon between Chinese and Japanese. This research built a Chinese-Japanese bilingual lexicon dictionary through English, taking English as a kind of pivot language.…”

Section: Previous Studies Using Hanzi-kanjimentioning

confidence: 99%

An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Chen

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYNamed Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora. key words: named entity translation equivalents acquisition, Chinese Hanzi and Japanese Kanji mapping table, inductive learning, monolingual corpora

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Section: Previous Studies Using Hanzi-kanjimentioning

confidence: 99%

An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Chen

et al. 2017

IEICE Trans. Inf. & Syst.

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“…for Chinese-to-Japanese translation, there are limited previous works but using pivot language [69,70]. To the best of our knowledge, this is the first work of using reordering method for Chinese-to-Japanese SMT.…”

Section: Language Dependent Reorderingmentioning

confidence: 99%

Syntax-Based Pre-reordering for Chinese-to-Japanese Statistical Machine Translation

Han

Martínez-Gómez

Miyao

2016

Hybrid Approaches to Machine Translation

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Bilingual phrases are the main building blocks in statistical machine translation (SMT) systems. At training time, the most likely word-to-word alignment is computed and several heuristics are used to extract these bilingual phrases. Although this strategy performs relatively well when the source and target languages have a similar word order, the quality of extracted bilingual phrases diminishes when translating between languages structurally different, such as Chinese and Japanese. Syntax-based reordering methods in preprocessing stage have been developed and proved to be useful to aid the extraction of bilingual phrases and decoding. For Chinese-to-Japanese SMT, we carry out a detailed linguistic analysis on word order differences of this language pair to improve the word alignment. Our main contribution is threefold: (1) We first adapt an existing pre-reordering method called Head-finalization (HF) [1] for Chinese (HFC) [2] to improve Chinese-to-Japanese SMT system's translation quality. HF is originally designed to reorder English sentences for English-to-Japanese SMT and it performs well. However, our preliminary experiments results reveal its disadvantages on reordering Chinese due to particular characteristics of languages. We thus refine HF to HFC based on a deep linguistic study. To obtain the required syntactic information, we use a head-driven phrase structure grammar (HPSG) parser for Chinese. Nevertheless, the follow-up error analysis from the pre-reordering experiment explores more issues that bring difficulties for further improvement on HFC, such as the tree operation restriction of binary tree, inconsistency on definition of linguistic term and so on. (2) We then propose an entire new pre-reordering framework which is using an unlabeled dependency parser to achieve additional improvements on reordering Chinese sentences to be like Japanese word orders.We refer to it as DPC [3] for short. In this method, we first identify blocks of Chinese words that demand reorderings, such as verbs and certain particles. Then, we detect the proper position which is the right-hand side of their rightmost object dependent, since our reordering principle is to reorder a Subject-Verb-Object (SVO) language to resemble a Subject-Object-Verb (SOV) language. Other types of particles are relocated in the last step. Unlike other reordering systems, the boundaries of verbal blocks and their rightmost object in DPC are defined only by the dependency tree and part-of-speech tags.Additionally, dismissing of using structural and punctuation border is another benefit for the reordering of the reported speech frequently occurring in news domain. The experiments show advantages of DPC over the SMT baseline (Moses) and our HFC systems.Important advantages of this method are the applicability of many reordering rules to other SVO and SOV language pairs as well as the availability of dependency parsers and POS-taggers for many languages. Considering our pre-reordering methods of HFC and DPC are linguistically-motivated,...

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“…However, as a representative of long-distance language pairs, translation from Chinese to Japanese has received little attention. Few previous studies focused on translation of this language pair, but used English as a pivot language (Wu and Wang 2007;Tsunakawa, Okazaki, Liu, and Tsujii 2009). …”

Section: Related Workmentioning

confidence: 99%

Unlabeled Dependency Parsing Based Pre-reordering for Chinese-to-Japanese SMT

Han

Martínez-Gómez

Miyao

et al. 2014

Journal of Natural Language Processing

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In statistical machine translation, Chinese and Japanese is a well-known long-distance language pair that causes difficulties to word alignment techniques. Pre-reordering methods have been proven efficient and effective; however, they need reliable parsers to extract the syntactic structure of the source sentences. On one hand, we propose a framework in which only part-of-speech (POS) tags and unlabeled dependency parse trees are used to minimize the influence of parse errors, and linguistic knowledge on structural difference is encoded in the form of reordering rules. We show significant improvements in translation quality of sentences in the news domain over state-ofthe-art reordering methods. On the other hand, we explore the relationship between dependency parsing and our pre-reordering method from two aspects: POS tags and dependencies. We observe the effects of different parse errors on reordering performance by combining empirical and descriptive approaches. In the empirical approach, we quantify the distribution of general parse errors along with reordering quality. In the descriptive approach, we extract seven influential error patterns and examine their correlations with reordering errors.

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A Chinese-Japanese Lexical Machine Translation through a Pivot Language

Cited by 6 publications

References 17 publications

An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Syntax-Based Pre-reordering for Chinese-to-Japanese Statistical Machine Translation

Unlabeled Dependency Parsing Based Pre-reordering for Chinese-to-Japanese SMT

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