3 ms·

> Heads simply don't have bears on them https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcSzPevc7Us3usObZEF6ykQBcsh2xTDBYR5friM7T2TTwhvvCChI https://encr

by staz 8y ago

> Heads simply don't have bears on them https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcSzPevc7Us3usObZEF6ykQBcsh2xTDBYR5friM7T2TTwhvvCChI https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcSzPe...

ppod 8y ago
Perhaps the man proceeding towards the grizzly had a negative economic outlook. There are literally hundreds of word-sense-disambiguation papers being published per year, and have there have been for about a decade. The state-of-the-art has been advancing relentlessly, and examples like the ones cited here are being zoomed past right now by the latest thing, which is even larger models pre-trained on unsupervised data. There will be a next thing, and for NLP this will be backgammon or chess to 2020s Go. Embodiment is a better criticism, but this article is still bad, and is written as though Intelligence were some essential soul-like property that agents either possess or don't. It is really amazing how often articles with this viewpoint repeat one particular trope: they put the word "understanding" in italics. Look out for it.
- freeone3000 8y ago
  But if you do an error analysis on even ShotgunWSD (Betnaru 2017), you get errors that humans would not produce - archaic favors, missing recent terms, confusing opposing meanings, ... When the average researcher can easily select the correct sense. And that's a DETERMINISTIC algorithm! Swapping ELMO for pure WordNet gives us slightly better results in that sense but introduces literal random chance errors. And the older, Bayesian models are worse! While we'll probably solve WSD to a high degree of accuracy (such that engineering bugs cause more failures than the model), our current approach is still fundamentally flawed. We need a better method of imbuing causal relationships than correlation+chance.
  - ppod 8y ago
    > archaic favors, missing recent terms, confusing opposing meanings, Humans make errors like these all the time (e.g., I don't know what you mean by 'favor' here, and I'm sure many people wouldn't have the type "ELMO"), and the dictionary definition of the word shifts accordingly, except we can't really call them 'errors' when it's a natural phenomenon that we're trying to match. Humans make seemingly random errors sometimes too, confusing one word for a mother, I mean another. When we do it we infer (perhaps correctly) deep psychological causes. Current machines don't have personal identities and desires and goals like we do, but embodied agents with a natural selection process could have those things, there isn't an obvious technological barrier. And if you build in a language model that shares some of the general computation machinery we could reasonably expect it to make those kinds of errors.
paganel 8y ago
> Heads simply don't have bears on them Also, humans may refer to other humans as toy-bears and get punished for it (https://www.theguardian.com/world/2018/aug/07/china-bans-winnie-the-pooh-film-to-stop-comparisons-to-president-xi https://www.theguardian.com/world/2018/aug/07/china-bans-win...). Human language is hard, metaphors are hard.

by staz 8y ago

ppod 8y ago

Perhaps the man proceeding towards the grizzly had a negative economic outlook. There are literally hundreds of word-sense-disambiguation papers being published per year, and have there have been for about a decade. The state-of-the-art has been advancing relentlessly, and examples like the ones cited here are being zoomed past right now by the latest thing, which is even larger models pre-trained on unsupervised data. There will be a next thing, and for NLP this will be backgammon or chess to 2020s Go. Embodiment is a better criticism, but this article is still bad, and is written as though Intelligence were some essential soul-like property that agents either possess or don't. It is really amazing how often articles with this viewpoint repeat one particular trope: they put the word "understanding" in italics. Look out for it.

freeone3000 8y ago
But if you do an error analysis on even ShotgunWSD (Betnaru 2017), you get errors that humans would not produce - archaic favors, missing recent terms, confusing opposing meanings, ... When the average researcher can easily select the correct sense. And that's a DETERMINISTIC algorithm! Swapping ELMO for pure WordNet gives us slightly better results in that sense but introduces literal random chance errors. And the older, Bayesian models are worse! While we'll probably solve WSD to a high degree of accuracy (such that engineering bugs cause more failures than the model), our current approach is still fundamentally flawed. We need a better method of imbuing causal relationships than correlation+chance.
- ppod 8y ago
  > archaic favors, missing recent terms, confusing opposing meanings, Humans make errors like these all the time (e.g., I don't know what you mean by 'favor' here, and I'm sure many people wouldn't have the type "ELMO"), and the dictionary definition of the word shifts accordingly, except we can't really call them 'errors' when it's a natural phenomenon that we're trying to match. Humans make seemingly random errors sometimes too, confusing one word for a mother, I mean another. When we do it we infer (perhaps correctly) deep psychological causes. Current machines don't have personal identities and desires and goals like we do, but embodied agents with a natural selection process could have those things, there isn't an obvious technological barrier. And if you build in a language model that shares some of the general computation machinery we could reasonably expect it to make those kinds of errors.

paganel 8y ago

> Heads simply don't have bears on them Also, humans may refer to other humans as toy-bears and get punished for it (https://www.theguardian.com/world/2018/aug/07/china-bans-winnie-the-pooh-film-to-stop-comparisons-to-president-xi https://www.theguardian.com/world/2018/aug/07/china-bans-win...). Human language is hard, metaphors are hard.