Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Paving the way for human-level sentence corrections

tech.grammarly.com

Post reply on HN
Print view View original

11–20 of 23 posts

Re: Paving the way for human-level sentence corrections

#11

Post by mack73 »

throwayedidqo wrote: post #6

I have a feeling this is one of those places where ML will not be useful until we have strong AI. Certain grammatical errors are impossible to fix unless you understand the overall meaning of the text. Sometimes this meaning is embedded over many paragraphs. Errors involving incorrect word usage are unsolvable when words have more than one meaning and you don't comprehend the subject at hand.

Once we have strong AI, whatever that buzz word means, what then would be the usefulness of understanding slang?

Personaly I think the usefulness is already to be able to enterpret a concept encoded in slang as the same as the concept derived from a message encoded in a different dialect (or language).

I would never assume a machine spoke this language, only that they understood it. Machines should evolve into speaking succinctly as to not include unneccessary complexity in their messages as they would strive to be well-understood like all other persons do. I fail to see why we would want to produce slang-encoded messages, unless we want to mask the fact we are a machine.

Re: Paving the way for human-level sentence corrections

#12

Post by throwayedidqo »

mack73 wrote: post #11
throwayedidqo wrote: post #6

I have a feeling this is one of those places where ML will not be useful until we have strong AI. Certain grammatical errors are impossible to fix unless you understand the overall meaning of the text. Sometimes this meaning is embedded over many paragraphs. Errors involving incorrect word usage are unsolvable when words have more than one meaning and you don't comprehend the subject at hand.

Once we have strong AI, whatever that buzz word means, what then would be the usefulness of understanding slang? Personaly I think the usefulness is already to be able to enterpret a concept encoded in slang as the same as the concept derived from a message encoded in a different dialect (or language). I would never assume a machine spoke this language, only that they understood it. Machines should evolve into speaki…

Ambiguous messages do not imply slang. Plenty of words have multiple meanings in normal and formal English. It's a much worse problem in tonal languages like Chinese. Tell me how you could grammatically correct this without understanding meaning https://en.m.wikipedia.org/wiki/Lion-Eating_Poet_in_the_Ston...

Strong AI isn't a buzzword either, it's been in use for as long as I can remember. Maybe you would be able to understand my Grammer better if I said super human general intelligence and wasted a bunch of space in the process.

I don't think you read my comment? You seem to imply that the corrections would be unambiguous while my point was that some errors are uncorrectable without understanding meaning.

Re: Paving the way for human-level sentence corrections

#13

Post by upboatter »

throwayedidqo wrote: post #6

I have a feeling this is one of those places where ML will not be useful until we have strong AI. Certain grammatical errors are impossible to fix unless you understand the overall meaning of the text. Sometimes this meaning is embedded over many paragraphs. Errors involving incorrect word usage are unsolvable when words have more than one meaning and you don't comprehend the subject at hand.

if comprehension at level don't exist, someone has incentive to correct those to lower level. I certainly do. We are not talking about poetry, are we?

Re: Paving the way for human-level sentence corrections

#14

Post by coldtea »

throwayedidqo wrote: post #6

I have a feeling this is one of those places where ML will not be useful until we have strong AI. Certain grammatical errors are impossible to fix unless you understand the overall meaning of the text. Sometimes this meaning is embedded over many paragraphs. Errors involving incorrect word usage are unsolvable when words have more than one meaning and you don't comprehend the subject at hand.

>Certain grammatical errors are impossible to fix unless you understand the overall meaning of the text. Sometimes this meaning is embedded over many paragraphs.

A non-strong AI can get clues to that meaning (without really understanding anything) based on the words in those previous and subsequent paragraphs, and a huge text corpus.

Re: Paving the way for human-level sentence corrections

#15

Post by kwhitefoot »

lutusp wrote: post #7

This is a great project -- in Phase One, the algorithm will correct sentences written by people who didn't learn basic literacy in school and who subsequently endeavor to avoid reading or writing any text, preferring video. In Phase Two, the algorithm will do away with the poorly written source and create something entirely on its own. Based on my sampling of contemporary human-crafted sentences, Phase Two will take…

> In Phase Two, the algorithm will do away with the poorly written source and create something entirely on its own. Based on my sampling of contemporary human-crafted sentences,

A problem with this is that there will be a tendency for it to become normative. This is what happened to the OED. Originally it was an etymological dictionary of the usage of English. Now it is regarded as an arbiter of 'correct' English.

Re: Paving the way for human-level sentence corrections

#16

Post by matt4077 »

aisofteng wrote: post #8

The problem that the authors are trying to tackle is an interesting and difficult one. I have noticed that when dealing with natural language using artificial intelligence / machine learning techniques, the work being done by computer scientists very often would have greatly benefitted from collaboration with a linguist or other sort of language expert, especially in the design phase of an experiment. This work is a…

Maybe they went to ten linguists, and all they got as an answer is "there is no objective definition of 'fluent'. You are trying to find a single correct result that doesn't exist!"

Then, armed with the naiveté of thinking that if there is something like 'fluency' it must be possible to measure it, they just threw a bit of money at the problem. Note that asking a representative group of people is the closest you can get to exactly what you want to measure (apart from asking everyone). It doesn't matter that there's no agreed-upon method to measure the quality of pizza: if I maximise the subjective impression, I'll get exactly what I wanted.

Re: Paving the way for human-level sentence corrections

#18

Post by matt4077 »

I don't quite get this... According to the bar graph, the automated systems fail to correct around half of even orthographic (spelling) mistakes. One example is "advertissment", which macOS is now trying really hard to correct against my will in this text area.

Another example is "From this scope, social media has shorten our distance", where "scope" is supposed to be "perspective". That seems to be something that machine learning should easily pick up on, and indeed, when I just tried it on google translate, I couldn't get it to make this mistake without my original (german) sentence also encroaching awkwardness.

So I'm unsure how much value it is to win against systems that fail rather spectacularly. I also don't quite understand why you would need manually-created data for this task, instead of just buying everything ever written in TOEFL essay questions and pitting it against the New York Time's archive.

It's obviously quite likely that there are good reasons for all this. They may have thought a bit longer about it than I just did.

Re: Paving the way for human-level sentence corrections

#19

Post by notahacker »

aisofteng wrote: post #8

The problem that the authors are trying to tackle is an interesting and difficult one. I have noticed that when dealing with natural language using artificial intelligence / machine learning techniques, the work being done by computer scientists very often would have greatly benefitted from collaboration with a linguist or other sort of language expert, especially in the design phase of an experiment. This work is a…

Using the Mechanical Turkers to rate fluency would arguably be an even more dubious evaluation benchmark if more rigorous standards and consistent for fluent English existed; people regarded as having good writing and editing skills can find better-paying sources of part-time remote work than AMT. Some of the examples of human editing shown in the blog entry certainly don't look fluent to me...

Re: Paving the way for human-level sentence corrections

#20

Post by 3131s »

throwayedidqo wrote: post #12
mack73 wrote: post #11

Earlier quoted context omitted.

Once we have strong AI, whatever that buzz word means, what then would be the usefulness of understanding slang? Personaly I think the usefulness is already to be able to enterpret a concept encoded in slang as the same as the concept derived from a message encoded in a different dialect (or language). I would never assume a machine spoke this language, only that they understood it. Machines should evolve into speaki…

Ambiguous messages do not imply slang. Plenty of words have multiple meanings in normal and formal English. It's a much worse problem in tonal languages like Chinese. Tell me how you could grammatically correct this without understanding meaning https://en.m.wikipedia.org/wiki/Lion-Eating_Poet_in_the_Ston... Strong AI isn't a buzzword either, it's been in use for as long as I can remember. Maybe you would be able to…

> Plenty of words have multiple meanings in normal and formal English.

There are some stats from Wordnet on polysemy in English. Obviously this depends on the granularity of a set of senses in a dictionary, but regardless English has many polysemous words (26,000+ according to Wordnet). And more importantly, these polysemous words also tend to be the most common words, hence words like "set" having around 120 definitions in the Oxford English dictionary.

https://wordnet.princeton.edu/wordnet/man/wnstats.7WN.html#s...

Post reply on HN