View source for Shanghainese and Mandarin Chinese

===Resources for machine translation between Shanghainese and Mandarin Chinese===
*Resources developed for [https://en.wikipedia.org/wiki/Shanghainese Shanghainese]:
** Link to resources developed for Shanghainese: [https://wikis.swarthmore.edu/ling073/Shanghainese]
*Resources developed for [https://en.wikipedia.org/wiki/Mandarin_Chinese Mandarin Chinese]:

===External Resources===
*Link to repository language pair:[https://github.swarthmore.edu/Ling073-sp25/ling073-wuu-zho.git]
*Link to Shanghainese repository transducer:[https://github.swarthmore.edu/Ling073-sp25/ling073-wuu.git]
*Link to Mandarin Chinese repository transducer:[https://github.com/apertium/apertium-zho.git]
*Link to Shanghainese to Mandarin Tatoeba Text: [https://tatoeba.org/en/sentences/search?from=wuu&query=&to=cmn]

Initial Evaluation==
===Sentences===
* (1) <code>wuu</code>: 我爱侬。
** <code>zho</code>: 我爱你。
** <code>eng</code>: I love you.
** lexical transfer output: ^我<prn><pers><p1><sg>/我<prn><pers><p1><sg>$ ^爱<vblex>/爱<vblex>$ ^侬<prn><pers><p2><sg>/你<prn><pers><p2><sg>$ 。 ^.<sent>/.<sent>$
** full translation output: 我 爱 你 。

* (2) <code>wuu</code>: 汏手间勒勒阿里耷？
** <code>zho</code>: 洗手间在哪里？
** <code>eng</code>: Where is the restroom?
** lexical transfer output: 洗手间 在 哪里 ？
** full translation output: ^汏手间<n>/洗手间<n>$ ^勒勒<pr>/在<pr>$ ^阿里耷<adv>/哪里<adv>$？^.<sent>/.<sent>

* (3) <code>wuu</code>: 我勿晓得。
** <code>zho</code>: 我不知道。
** <code>eng</code>: I don't know.
** lexical transfer output: ^我<prn><pers><p1><sg>/我<prn><pers><p1><sg>$ ^勿<adv>/不<adv>$ ^晓得<vblex>/知道<vblex>$。^.<sent>/.<sent>
** full translation output: 我 不 知道 。

* (4) <code>wuu</code>: 伊啥人啊？
** <code>zho</code>: 他是谁？
** <code>eng</code>: Who are they? (sing.)
** lexical transfer output: ^伊<prn><pers><p3><sg>/他<prn><pers><p3><sg><m>/她<prn><pers><p3><sg><f>/它<prn><pers><p3><sg><nt>$ ^啥人<prn><itg>/谁<prn>$ ^啊<part>/啊<qst>$？^.<sent>/.<sent>$
** full translation output: 他 #谁 啊 ？

* (5) <code>wuu</code>: 侬要吃茶还是吃咖啡啊？
** <code>zho</code>: 你想喝茶还是喝咖啡？
** <code>eng</code>: Do you want to drink tea or coffee?
** lexical transfer output: ^侬<prn><pers><p2><sg>/你<prn><pers><p2><sg>$ ^要<vaux>/要<vblex>$ ^吃<vblex>/喝<vblex>/吃<vblex>$ ^茶<n>/茶<n>$ ^还是<cnjcoo>/还是<cnjcoo>$ ^吃<vblex>/喝<vblex>/吃<vblex>$ ^咖啡<n>/咖啡<n>$ ^啊<part>/啊<qst>$？^.<sent>/.<sent>
** full translation output: 你 要 喝 茶 #还是 喝 咖啡 啊 ？

* (6) <code>wuu</code>: 侬要走啦？
** <code>zho</code>: 你要走了吗？
** <code>eng</code>: Are you leaving?
** lexical transfer output: ^侬<prn><pers><p2><sg>/你<prn><pers><p2><sg>$ ^要<vaux>/要<vblex>$ ^走<vblex>/走<vblex>$ ^啦<part>/啦<qst>$ ^.<sent>/.<sent>
** full translation output: 你 要 走 啦 ？

* (7) <code>wuu</code>: 勿要紧个！
** <code>zho</code>: 没关系。
** <code>eng</code>: Don't worry.
** lexical transfer output: ^勿要紧<ij>/没关系<ij>$ ^个<part>/的<pr>$！^.<sent>/.<sent>
** full translation output: #没关系 的 ！

* (8) <code>wuu</code>: 我一只也勿要。
** <code>zho</code>: 我一个也不要。
** <code>eng</code>: I don't want even one.
** lexical transfer output: ^我<prn><pers><p1><sg>/我<prn><pers><p1><sg>$ ^一<num>/一<num>$ ^只<cls>/个<num>/只<num>$ ^也<adv>/也<adv>$ ^勿<adv>/不<adv>$ ^要<vaux>/要<vblex>$。^.<sent>/.<sent>
** full translation output: 我 一 个 也 不 要 。

* (9) <code>wuu</code>: 今朝天好。
** <code>zho</code>: 今天好天气。
** <code>eng</code>: The weather's nice today.
** lexical transfer output: ^今朝<n>/今天<n>$ ^天<n>/天<n>$ ^好<adj>/好<adj>$。^.<sent>/.<sent>
** full translation output: 今天 天 好 。

* (10) <code>wuu</code>: 一、二、三、四、五、六、七、八、九、十。
** <code>zho</code>: 一、二、三、四、五、六、七、八、九、十。
** <code>eng</code>: One, two, three, four, five, six, seven, right, nine, ten.
** lexical transfer output: ^一<num>/一<num>$、^二<num>/二<num>$、^三<num>/三<num>$、^四<num>/四<num>$、^五<num>/五<num>$、^六<num>/六<num>$、^七<num>/七<num>$、^八<num>/八<num>$、^九<num>/九<num>$、^十<num>/十<num>$。^.<sent>/.<sent>
** full translation output: 一、二、三、四、五、六、七、八、九、十。

===Polished RBMT System===

==Additions==
* 2 new lexical selection rules
* 3 new disambiguation rules 
* 2 new transfer rules

==Final Evaluation==
*Shanghainese Transducer
**Precision: Coverage hfst is still broken
**Recall: Coverage hfst is still broken
**Number of words in Large Corpus: Coverage hfst is still broken
**Number of stems in Transducer: Coverage hfst is still broken

*<code>wuu</code> → <code>zho</code>
**WER: apertium-eval-translator-line does not work on space-less orthography 
**PER: apertium-eval-translator-line does not work on space-less orthography 
**Proportion of stems translated correctly in Longer Corpus: apertium-eval-translator-line does not work on space-less orthography 
**Trimmed coverage over longer and large corpora: apertium-eval-translator-line does not work on space-less orthography 
**The number of tokens in longer and large corpora: apertium-eval-translator-line does not work on space-less orthography 

[[Category:Shanghainese]]
[[Category:Sp25_TranslationPairs]]