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		<id>http://index.cslt.org/mediawiki/index.php?action=history&amp;feed=atom&amp;title=NLP_Status_Report_2017-4-10</id>
		<title>NLP Status Report 2017-4-10 - 版本历史</title>
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		<updated>2026-04-07T19:56:54Z</updated>
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	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=NLP_Status_Report_2017-4-10&amp;diff=26852&amp;oldid=prev</id>
		<title>2017年5月3日 (三) 02:52 Zhangsy</title>
		<link rel="alternate" type="text/html" href="http://index.cslt.org/mediawiki/index.php?title=NLP_Status_Report_2017-4-10&amp;diff=26852&amp;oldid=prev"/>
				<updated>2017-05-03T02:52:28Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class='diff diff-contentalign-left'&gt;
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				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;←上一版本&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;2017年5月3日 (三) 02:52的版本&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第25行：&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第25行：&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|-&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|-&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|Shiyue Zhang || &amp;#160;&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|Shiyue Zhang || &amp;#160;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;−&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* &lt;del class=&quot;diffchange diffchange-inline&quot;&gt;got a reasonable baseline &lt;/del&gt;on &lt;del class=&quot;diffchange diffchange-inline&quot;&gt;big zhen data&lt;/del&gt;&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;working &lt;/ins&gt;on &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;a paper for EMNLP &lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;||&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;||&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;−&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* &lt;del class=&quot;diffchange diffchange-inline&quot;&gt;implement mem model &lt;/del&gt;on &lt;del class=&quot;diffchange diffchange-inline&quot;&gt;this baseline, and test on big data&lt;/del&gt;&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;working &lt;/ins&gt;on &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;a paper for EMNLP &lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|-&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|-&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|Peilun Xiao ||&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;|Peilun Xiao ||&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;/table&gt;</summary>
		<author><name>Zhangsy</name></author>	</entry>

	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=NLP_Status_Report_2017-4-10&amp;diff=26847&amp;oldid=prev</id>
		<title>Zhangjy：以“{| class=&quot;wikitable&quot; !Date !! People !! Last Week !! This Week |- | rowspan=&quot;6&quot;|2017/4/5 |Yang Feng || * Got the sampled 100w good data and ran Moses (BLEU: 30.6) *...”为内容创建页面</title>
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				<updated>2017-05-03T02:41:45Z</updated>
		
		<summary type="html">&lt;p&gt;以“{| class=&amp;quot;wikitable&amp;quot; !Date !! People !! Last Week !! This Week |- | rowspan=&amp;quot;6&amp;quot;|2017/4/5 |Yang Feng || * Got the sampled 100w good data and ran Moses (BLEU: 30.6) *...”为内容创建页面&lt;/p&gt;
&lt;p&gt;&lt;b&gt;新页面&lt;/b&gt;&lt;/p&gt;&lt;div&gt;{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
!Date !! People !! Last Week !! This Week&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;6&amp;quot;|2017/4/5&lt;br /&gt;
|Yang Feng ||&lt;br /&gt;
* Got the sampled 100w good data and ran Moses (BLEU: 30.6)&lt;br /&gt;
* Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s_i-1 as memory query; 2. use s_i-1+c_i as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention.&lt;br /&gt;
* ran experiments for the above steps but the loss was inf. I am looking for reasons.&lt;br /&gt;
||&lt;br /&gt;
*do experiments and write the paper&lt;br /&gt;
|-&lt;br /&gt;
|Jiyuan Zhang ||&lt;br /&gt;
*convert the style of the paper to EMNLP&lt;br /&gt;
*contact the ppg's author to get the code&lt;br /&gt;
|| &lt;br /&gt;
*improve the effect of the qx's model&lt;br /&gt;
|-&lt;br /&gt;
|Andi Zhang ||&lt;br /&gt;
*revise the original oov model so that it can automatically detect oov words and translate them &lt;br /&gt;
*deal with the situation that source word is oov but target word is not oov first&lt;br /&gt;
*it didn't predict right&lt;br /&gt;
||&lt;br /&gt;
*make the model work as what we wanted&lt;br /&gt;
*deal with the situation that source word is oov and target word is also oov, then other situations&lt;br /&gt;
|-&lt;br /&gt;
|Shiyue Zhang || &lt;br /&gt;
* got a reasonable baseline on big zhen data&lt;br /&gt;
||&lt;br /&gt;
* implement mem model on this baseline, and test on big data&lt;br /&gt;
|-&lt;br /&gt;
|Peilun Xiao ||&lt;br /&gt;
&lt;br /&gt;
||&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Zhangjy</name></author>	</entry>

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