<p>Natural language understanding systems can be based on <a href="https://en.wikipedia.org/wiki/Discourse_representation_theory" rel="nofollow noreferrer">discourse representation</a> theories, which represent the meaning of English sentences as first-order logical predicates. <a href="https://en.wikipedia.org/wiki/Attempto_Controlled_English" rel="nofollow noreferrer">Attempto Controlled English</a> is one example of a natural language understanding system that relies on discourse representation theories.</p>

<p>Similarly, there are several implementations of <a href="http://www.cs.utexas.edu/~ml/publications/area/77/learning_for_semantic_parsing" rel="nofollow noreferrer">semantic parsers</a> that convert natural-language texts into formal representations of their meanings.</p>

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2018-02-04T15:31:12.347Z
2018-02-04T15:31:12.347Z
Anderson Green
<p><strong>What is the definition of the understanding of (written) natural language and how can we test or measure this understanding?</strong></p>

<p>Natural language understanding is neglected part of natural language processing in computer science. Exact definitions and tests of understanding are necessary for producing artificial general intelligence (AGI) - e.g. <a href="https://link.springer.com/chapter/10.1007/978-3-319-41649-6_13" rel="noreferrer">https://link.springer.com/chapter/10.1007/978-3-319-41649-6_13</a> article argues that artificial general intelligence (e.g. software piece) should learn and self-modify itself but such development is possible only if AGI can estimate the quality/fitness of the new self-modification - whether this self-modification is better than existing version of AGI and whether this self-modification can be applied? Obviously - if we want to apply machine learning and AGI to understanding of the natural language then we should be able to test and measure the understanding.</p>

<p>There is article <a href="https://link.springer.com/chapter/10.1007/978-3-319-41649-6_11" rel="noreferrer">https://link.springer.com/chapter/10.1007/978-3-319-41649-6_11</a> about understanding specifically but it is somehow narrow minded to the particular QA task. Maybe cognitive science have better tests?</p>

<p>For the reference - there are some AGI systems of "cognitive architectures" under development (wikipedia has list of them) - like OpenCog, NARS, Soar and some others.</p>

<cognitive-modeling><language><artificial-intelligence><metacognition><language-processing>
What is understanding of natural language and how can we test or measure it?
TomR
# What is understanding of natural language and how can we test or measure it?

(Asked by: username_0 on 2017-03-10)

**What is the definition of the understanding of (written) natural language and how can we test or measure this understanding?**

Natural language understanding is neglected part of natural language processing in computer science. Exact definitions and tests of understanding are necessary for producing artificial general intelligence (AGI) - e.g. <https://link.springer.com/chapter/10.1007/978-3-319-41649-6_13> article argues that artificial general intelligence (e.g. software piece) should learn and self-modify itself but such development is possible only if AGI can estimate the quality/fitness of the new self-modification - whether this self-modification is better than existing version of AGI and whether this self-modification can be applied? Obviously - if we want to apply machine learning and AGI to understanding of the natural language then we should be able to test and measure the understanding.

There is article <https://link.springer.com/chapter/10.1007/978-3-319-41649-6_11> about understanding specifically but it is somehow narrow minded to the particular QA task. Maybe cognitive science have better tests?

For the reference - there are some AGI systems of "cognitive architectures" under development (wikipedia has list of them) - like OpenCog, NARS, Soar and some others.

**Tags:** cognitive-modeling, language, artificial-intelligence, metacognition, language-processing


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## 1 answers

### Answer by username_1 on 2018-02-04. Score: 2

Natural language understanding systems can be based on [discourse representation](https://en.wikipedia.org/wiki/Discourse_representation_theory) theories, which represent the meaning of English sentences as first-order logical predicates. [Attempto Controlled English](https://en.wikipedia.org/wiki/Attempto_Controlled_English) is one example of a natural language understanding system that relies on discourse representation theories.

Similarly, there are several implementations of [semantic parsers](http://www.cs.utexas.edu/~ml/publications/area/77/learning_for_semantic_parsing) that convert natural-language texts into formal representations of their meanings.


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