<story><title>Using AI to match human performance in translating news from Chinese to English</title><url>https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/</url></story><parent_chain></parent_chain><comment><author>anewhnaccount2</author><text>Compare this to one of Google&amp;#x27;s blog post promoting their MT research: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;research.googleblog.com&amp;#x2F;2016&amp;#x2F;09&amp;#x2F;a-neural-network-for-machine.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;research.googleblog.com&amp;#x2F;2016&amp;#x2F;09&amp;#x2F;a-neural-network-for...&lt;/a&gt;&lt;p&gt;It is:&lt;p&gt;1) More accurate, compared to hyperbole like e.g. &amp;quot;Bridging the Gap between Human and Machine Translation&amp;quot; we have right there in the title the domain: news.&lt;p&gt;2) A more impressive result. This result is on an independently set up evaluation framework, compared to Google&amp;#x27;s which used their own framework.&lt;p&gt;Compare further the papers:
&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1609.08144.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1609.08144.pdf&lt;/a&gt;
&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.microsoft.com&amp;#x2F;en-us&amp;#x2F;research&amp;#x2F;uploads&amp;#x2F;prod&amp;#x2F;2018&amp;#x2F;03&amp;#x2F;final-achieving-human.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.microsoft.com&amp;#x2F;en-us&amp;#x2F;research&amp;#x2F;uploads&amp;#x2F;prod&amp;#x2F;2018&amp;#x2F;0...&lt;/a&gt;&lt;p&gt;These researcher appear to have been much clearer about what they&amp;#x27;re actually claiming, and also used more standard evaluation tools (Appraise) and methodology rather than something haphazardly hacked together.</text></comment>
<story><title>Using AI to match human performance in translating news from Chinese to English</title><url>https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/</url></story><parent_chain></parent_chain><comment><author>d--b</author><text>As impressive as it may be, these people should refrain from claiming &amp;#x27;human-like&amp;#x27; translation from a system that has no way of &amp;#x27;knowing&amp;#x27; anything about context, other than statistical occurrences.&lt;p&gt;It is certain that, on occasion, the system will make such mistakes as stating the opposite of what is being said in the first place, or attribute one action to the wrong person, and what not. Perhaps on average it&amp;#x27;s as good as a person, but this system will make mistakes that disqualifies it from being used without a bucket of salt.</text></comment>