1. Cross-language Projection of Dependency Trees Based on Constrained Partial Parsing for Tree-to-Tree Machine Translation Yu Shen, Chenhui Chu, Fabien Cromieres and Sadao Kurohashi 2016/8/11 @ WMT 2016

2. Tree-to-Tree MT (KyotoEBMT) [Richardson+ 2015] 2 作为 测量 器械 使用 了 秒表 Input: 作为测量器械使用 了秒表 Translation Example Database Output: 測定機器としては ストップウォッチを用 いた 作为 使用 了 变位 操作者 オペレータ して は 変位 と を 用いた 機器 して は ストップウォッチ と を 用いた 測定 使用 秒表 ストップウォッチ を 使った 输入 器械 入力 機器 测量 频率 測定 頻度 ・・・・・ Translation rules must be continuous in trees, and thus isomorphic trees are preferred!

3. sap ‘s adsorption behavior preferably only include standard sample liquid adsorption behavior similar ‘s K + K + sap‘s behaviorextremelysimilarandonlyinclude‘sstandardsampleliquid‘sbehaviorK++K Japanese (HQ) Chinese(LQ) adsorption Parsing error Annotation criterion difference Our solution: projection 3

4. sap ‘s adsorption behavior preferably only include standard sample liquid adsorption behavior similar ‘s K + K + sap‘s behaviorextremelysimilarandonlyinclude‘sstandardsampleliquid‘sbehaviorK++K Japanese (HQ) Chinese(LQ) adsorption Proposed method: Constrained partial parsing Alignment error Null alignment Parsing error 4

5. Partial parsing Parser training data Partial projection Re-trained Partial parsing High-quality tree (e.g. Ja) Low-quality tree (e.g. Zh) Projected tree Partially projected tree 0 1 23 0 2 1 3 0 2 1 3 0 2 1 3 5 Proposed Method

6. sap ‘s adsorption behavior preferably only include standard sample liquid adsorption behavior similar ‘s K + K + sap‘s behaviorextremelysimilarandonlyinclude‘sstandardsampleliquid‘sbehaviorK++K Japanese (HQ) Chinese(LQ) adsorption Partial projection K + + K Null alignment Alignment error Parsing error Non-projective 6

7. sap ‘s adsorption behavior preferably only include standard sample liquid adsorption behavior similar ‘s K + K + sap‘s behaviorextremelysimilarandonlyinclude‘sstandardsampleliquid‘sbehaviorK++K Japanese (HQ) Chinese(LQ) adsorption K + + K Partial Parsing 7

8. sap ‘s adsorption behavior preferably only include standard sample liquid adsorption behavior similar ‘s K + K + sap‘s behaviorextremelysimilarandonlyinclude‘sstandardsampleliquid‘sbehaviorK++K Japanese (HQ) Chinese(LQ) adsorption Re-trained Partial Parsing K + + K ✔ ✔ 8

9. Experimental Settings MT Tasks – WAT Chinese (LQ) - Japanese (HQ) (scientific domain) [Nakazawa+ 2015] – NTCIR Chinese (LQ) - English (HQ) (patent domain) [Goto+ 2013] Baseline tree-to-tree MT system – KyotoEBMT [Richardson+ 2015] 9

10. MT Results (BLEU-4) SystemJa-to-ZhZh-to-JaEn-to-ZhZh-to-En Moses phrase based27.2533.9426.9029.64 Moses string-to-tree26.20N/A28.69N/A Moses tree-to-stringN/A33.49N/A30.98 Baseline KyotoEBMT29.3334.7327.4628.66 Baseline partial parsing30.1235.8427.1229.89 Re-trained partial parsing30.2836.1827.8330.00 10

11. Number of Matching Translation Rules for the Test Sentences SystemJa-to-ZhZh-to-JaEn-to-ZhZh-to-En Baseline KyotoEBMT13.13M8.43M8.77M9.17M Baseline partial parsing15.69M9.88M9.35M10.35M Re-trained partial parsing15.69M9.90M9.35M10.43M 11

12. InputOutputInputOutput Baseline KyotoEBMTRe-trained partial parsing 12 An Improved Example [NN]

13. InputOutputInputOutput Baseline KyotoEBMTRe-trained partial parsing A Worsened Example 13 [VV]

14. Related Work Projection for improving parsing accuracy – Address alignment errors etc.: train a target parser on the partially projected trees [Ganchev+ 2009; Rasooli+ 2015 etc.] – Adjust annotation criteria from source to target trees [Hwa+ 2005; Jiang+ 2011 etc.] Projection for MT – String-to-string: projection for pre-ordering [Goto+ 2015] – Tree-to-string: guide a supervised parser with the projected parser [Jiang+ 2010] – Tree-to-tree: partial projection with back tracking [Shen+ 2015] 14

15. Conclusion Proposed constrained partial parsing for projection dependency trees Showed the effectiveness in tree-to-tree MT Future Work – Perform partial parsing iteratively – Project POS tags 15