This is the
talk page for discussing improvements to the
Natural language understanding article. This is not a forum for general discussion of the article's subject. |
Article policies
|
Find sources: Google ( books · news · scholar · free images · WP refs) · FENS · JSTOR · TWL |
![]() | A fact from Natural language understanding appeared on Wikipedia's
Main Page in the
Did you know column on 1 March 2010 (
check views). The text of the entry was as follows:
| ![]() |
![]() | This article is rated Start-class on Wikipedia's
content assessment scale. It is of interest to the following WikiProjects: | |||||||||||||||||||||||
|
|
rm:
Steps of NLU:
as if there was a canonical NLU methodology. rm
===Approaches==-
it's equally vacuous. When someone gives the article attention the first will be mentioned and as for the second ... (rhetorical ellipsis). —Preceding unsigned comment added by 74.78.162.229 ( talk) 20:29, 10 July 2008 (UTC)
FTR, this really has relatively little to do with the subject of this article. Understanding proceeds indifferently from spoken or written speech, except insofar as non-verbal communications are concerned and of course these are outside the normal scope of either NLP or SR.
74.78.162.229 ( talk) 19:57, 10 July 2008 (UTC)
This article can be kindly described as "hopeless". It has a few irrelevant short paragraphs and needs a 99.9999% rewrite. If no one objects, I will rewrite from scratch. There is nothing here that can be used. And there is NO point in merging with Natural language processing since this is "a field onto itself" and the merger will be the blind leading the blind, for the other article is no gem either. History2007 ( talk) 20:40, 18 February 2010 (UTC)
The second paragraph of the opening section makes the (albeit well-argued) unsupported claim that understanding is more complex than generation. While this might be true, it isn't cited or referenced.
I'm inclined to be believe that this isn't true, though. In a recent computational linguistics course I took, my professor repeatedly mentioned that good generation is much more difficult, because in understanding all of the information is there and only needs to be picked apart, whereas in generation, the computer has to make many "decisions" based on little else but context.
Anyway, I would consider removing this section until a source is found? I'm not sure if it adds a lot to the article, anyway. Thoughts? Pixor ( talk) 17:06, 17 June 2012 (UTC)
While the argumentation given is true, whether NLG is more difficult than NLU depends on the representation from which language is generated, and how much variation you want to put in the generation. I would not consider illing up slots in a template as proper NLG.
If you know how to get from a natural language to an abstract language representation, then you only have to reverse the process in order to do the output. So output is not more complex than input (this is case 1). On the other hand, if you know how to output, it means either your output scheme is at least as good as your input scheme (we're then back to case 1), or you know one way to output and that doesn't give you any guarantee regarding the input schemes. Still not convinced? When you input, you may face rules you don't know about, which makes the whole thing very difficult (often impossible, so you have to reject the phrases or take chances). When you output, you use a well-defined representation of the language, you know the semantics, you have a well-defined set of rules, and the only difficult part is to make sure you don't output something that has a multiple meanings. Then, one might argue that we could be taking about a crazy natural language in which basically everything is ambiguous. In that case, we can just fall back to the worse case scenario: case 1. Sparkles ( talk) 23:00, 24 January 2018 (UTC)
The process of disassembling and parsing input is more complex than the reverse process of assembling output in natural language generation because of the occurrence of unknown and unexpected features in the input and the need to determine the appropriate syntactic and semantic schemes to apply to it, factors which are pre-determined when outputting language. dubious – discuss
Quite apart from whether this is dubious, the case is argued on a strange foundation, and surely some proper citations are demanded for a contention such as this.
For me, excision until properly reworked is a slam dunk.
Hiding in the foliage is Postel's law: that we tend to be generous in dealing with messy inputs, while stricter in generating outputs. But in a natural language setting, why should this be a default assumption? Why should the natural language system be an inherent complexity filter? Much of the messiness of natural language input actually conveys sophisticated social nuance, so the output is only simpler than the input if we're happy for the output to lack nuance.
Because of our historical relationship with our machines, we tend to accept this bargain automatically. But this social criteria should not then be baked into a sweeping statement that input is harder than output. It's almost certain that human speech generation contains a feedback loop back to the input system (which is in turn informed by the mirror neurons, and our cognitive model of the speech recipient(s)) in order to assess the inherent trade-offs between economy of utterance, tonal sideband, and risky ambiguity. Any good writer knows that careful output is way harder than parsing input.
In the interpreter community, it's taken for granted that interpreters input their foreign language(s), and output their birth language(s), because you so need to know precisely what you just meant at all possible levels.
I'm pretty sure this was the talk where I heard an interpreter (this one seems very good at her trade) talking about making an exception to this rule: she actually does translate into her foreign language(s), out of a pure numbers game for the less common EU languages. But in the big languages (English, Spanish, German, French, Italian) this would never be allowed, because you can always find enough native-speaking translators with proficiency in any number of other EU languages.
Note also that she talks about (in this video, or one of her others on YouTube) how realtime translators have a very brief duty cycle. It's something like twenty minutes out of every hour (so you need 3× coverage for each language pair), because the task is so cognitively demanding that their brains explode. (She says that one ear is constantly devoted to listening back to what you just spoke, while the other ear is listening to the input and trying not to miss a single important word.)
Basically, we apply a very high standard to human input/output processes in a political context such as big EU gatherings, by which point no-one pretends that output is easier than input.
And then we implicitly give our machines a free pass, because we've all been weening on the social media business model that barely good enough is fan-effing-tastic (so long as it's free of direct monetary costs).
Input easy/output hard is completely crazy talk, and OR, and uncited, and a disservice to the larger article context.
There are definitely sober claims to be made about what will move the dial forward in the short term (and input will probably present more challenges in the short run), but that needs to be what we write here, and not this naive, sententious argumentation. — MaxEnt 18:02, 14 April 2018 (UTC)
The comment(s) below were originally left at Talk:Natural language understanding/Comments, and are posted here for posterity. Following several discussions in past years, these subpages are now deprecated. The comments may be irrelevant or outdated; if so, please feel free to remove this section.
* It's a stub! 74.78.162.229 ( talk) 20:14, 10 July 2008 (UTC) |
Last edited at 20:14, 10 July 2008 (UTC). Substituted at 00:57, 30 April 2016 (UTC)
Why is Searle's POV mentioned here specifically in respect to Watson? The placement of the citation gives the impression that he believes that *that* set of algorithms running on Watson failed to understand, rather than his more general epistemological view that no matter what algorithms were implemented it *couldn't* understand, which he posited decades before Watson. I don't believe any NLU researcher, or the Watson team would claim they are in any way addressing the Chinese room argument in their work. The word 'understanding' simply implies that they are working at the semantic level, rather than surface syntax or morphology, for example. The problem they are addressing is technical- not philosophical, and I don't think Searle's position is relevant except to demonstrate that claims a machine understands as people do is very shaky ground. It may be more useful to mention Searle in a section clarifying what 'understanding' means in this context. — Preceding unsigned comment added by 217.42.112.171 ( talk) 12:27, 1 August 2016 (UTC)
Watson revealed a huge increase in computational power and an ingenious program. I congratulate IBM on both of these innovations, but they do not show that Watson has superior intelligence, or that it's thinking, or anything of the sort. Computational operations, as standardly defined, could never constitute thinking or understanding for reasons that I showed over 30 years ago with a simple argument.
@ Mvliguori: This paragraph is somewhat confusing, since it introduces many phrases without explaining their relevance to natural language processing. Can you provide any reliable sources for this paragraph? Jarble ( talk) 01:56, 4 January 2019 (UTC) @ Jarble: Hello, I'm new to editing the wiki and I don't have my full barrings, I was hoping to use the Vognition Wiki Page to justify the text, but I have not finished it yet. I'd like to point out that I'm not describing NLP. I'm describing a NLU. I describe what a NLU is this way because it comes from the issued patent I invented, https://patents.google.com/patent/US9342500B2/en Please advise. Mvliguori 11:54, 4 January 2019 (UTC)
NLU is the post-processing of text, after the use of NLP algorithms (identifying parts-of-speech, etc.), that utilizes context from recognition devices (automatic speech recognition [ASR], vision recognition, last conversation, misrecognized words from ASR, personalized profiles, microphone proximity etc.), in all of its forms, to discern meaning of fragmented and run-on sentences to execute an intent from typically voice commands. NLU has an ontology around the particular product vertical that is used to figure out the probability of some intent. An NLU has a defined list of known intents that derives the message payload from designated contextual information recognition sources. The NLU will provide back multiple message outputs to separate services (software) or resources (hardware) from a single derived intent (response to voice command initiator with visual sentence (shown or spoken) and transformed voice command message too different output messages to be consumed for M2M communications and actions).
I've marked two aspects of this paragraph that fall short concision and clarity without trying to cover the whole enchilada (((((there are (six) parentheticals, including a nested parenthetical in this short passage))))).
In my experience the vast majority of authoritative mentions of NLU do not use a hyphen. I don't know why this page has one. It also has a hyphen in NLP. If there are no concerns, I will take these out. Jmill1806 ( talk) 11:34, 1 March 2024 (UTC)