Mimehand II[1] [2] 1 Suzuki [3] [3] [4] (1) (2) 1 [5] (3) 50 (4) 指文字, 3% (25 個 ) 漢字手話 + 指文字, 10% (80 個 ) 漢字手話, 43% (357 個 ) 地名 漢字手話 + 指文字, 21

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1 transliteration Machine translation of proper names from Japanese to Japanese Sign Language Taro Miyazaki 1 Naoto Kato 1 Hiroyuki Kaneko 1 Seiki Inoue 1 Shuichi Umeda 1 Toshihiro Shimizu 1 Nobuyuki Hiruma 1 Yuji Nagashima 2 Abstract: This paper describes machine translation methods of proper names from Japanese to Japanese Sign Language (JSL). Proper names, such as place names and person s names, appear frequently in news and weather information and are important to be translated precisely to JSL in our target domain. However, there are few researches on machine translation of proper names to JSL. The conventional methods of proper name translation into foreign language is transliteration, which is based on the phonetics of proper names. On the other hands, the translation into JSL is not based on the phonetics of proper names, but on Kanji-represented sign words in many cases. In this paper, we present machine translation methods based on Kanji-represented sign words to translate place names and person s names into JSL. We conducted a series of subjective evaluation experiments and obtained good results. Keywords: Machine Translation, Japanese Sign Language, Proper names, Kanji represented sign word 1. 1 NHK NHK Science and Technology Research Laboratories {miyazaki.t-jw, katou.n-ga, kaneko.h-dh, inoue.s-li, umeda.s-hg, shimizu.t-hy, 2 Kogakuin University c 2012 Information Processing Society of Japan 1

2 Mimehand II[1] [2] 1 Suzuki [3] [3] [4] (1) (2) 1 [5] (3) 50 (4) 指文字, 3% (25 個 ) 漢字手話 + 指文字, 10% (80 個 ) 漢字手話, 43% (357 個 ) 地名 漢字手話 + 指文字, 21% (90 個 ) 指文字, 14% (59 個 ) 人名 ( 名字 ) 固定訳, 44% (366 個 ) 漢字手話, 60% 人名 (260 個 ) 固定訳, 5% (24 個 ) [6] 4 1 *1 1 *1 c 2012 Information Processing Society of Japan 2

3 文字を入力 rule1 rule2 rule3 rule4 入力漢字以外の漢字が使われていない読みが一致する入力漢字が含まれる読みが近い単語があるか? 単語があるか? 単語があるか? 単語があるか? [3] ,000 11, *2 rule1 rule2 rule3 rule1 rule4 *2 / / / / /... / / / /......) c 2012 Information Processing Society of Japan 3

4 文字を入力 入力文字が使われている単語を単語単位の学習結果から抽出 手話単語を集計 結果の出力 ( 例 ) 大 大きい大臣大島大阪大村大田 { 大きい } { 首 }{ 長 } { とても }{ 島 } { 大阪 } { とても }{ 村 } { とても }{ 田 } { とても }:3 { 大きい }:1 { 首 }:1 { 長 }:1 { 島 }:1 { 大阪 }:1 大 { とても } 3 / / /... / / /......) / / ( 4) 1 / / / ,8, B rule1 8 rule2 5 rule3 3 1 rule C 8 8,7,6...1 C c 2012 Information Processing Society of Japan 4

5 固有名詞を入力 文字ごとに分割 手法 (A) 手法 (B) 手法 (C) 手法 (D) 文字単位の ルールベース翻訳 学習データを用いた統計的翻訳手法 全国地名手話マップ を学習データとした統計的翻訳手法 単語単位の学習データを用いた統計的翻訳手法 スコアリングスコアリングスコアリングスコアリング 文字ごとの翻訳結果を統合 翻訳結果を出力 D A+B+C+D) (A+B) [7] ID I 44 2 II III I II III c 2012 Information Processing Society of Japan 5

6 4 5 I II III ( 1 ) ( 2 ) ( 3 ) [1] Mimehand II [2] I-1 pp [3] 18 E3-1 pp [4] 2011 [5] 2006 [6] 16 PA2-5 pp [7], jjksiro/kensaku.html c 2012 Information Processing Society of Japan 6

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