Computational Linguistics: Crash Course Linguistics #15
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Date: 2022-04-04
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Comments and reviews: 10
Jean-Loup
I think the hardest aspect of computational linguistics is to determine the intent behind any chosen word or sentence in any given language. Unless you read from a text, which a TTS program can do easily, all the things we say or write have an underlying intent whether we're clearly aware of it or not. And that is something we can't really teach computers. Perhaps if we ever work out how to program sentience but until then, your Google Assistant is unlikely to ever speak like J. A. R. V. I. S.
Then you have some languages which are very different in style in their written form and in their spoken form: automatic translation of a text and a voice response to a spoken query from an assistant would require the use of the -proper- style. Then of course you have accents and dialects to take into account. In effect, I found in recent years we have to adapt our language to address assistants such as Siri, Google or Alexa because they only understand certain things formulated in a specific way, in other words, rather than teach machines to be more human, we end up being more machine when addressing them. I'm not sure whether this is a good thing or a bad thing but I wouldn't be surprised if it had consequences on language evolution in the long term.
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I think the hardest aspect of computational linguistics is to determine the intent behind any chosen word or sentence in any given language. Unless you read from a text, which a TTS program can do easily, all the things we say or write have an underlying intent whether we're clearly aware of it or not. And that is something we can't really teach computers. Perhaps if we ever work out how to program sentience but until then, your Google Assistant is unlikely to ever speak like J. A. R. V. I. S.
Then you have some languages which are very different in style in their written form and in their spoken form: automatic translation of a text and a voice response to a spoken query from an assistant would require the use of the -proper- style. Then of course you have accents and dialects to take into account. In effect, I found in recent years we have to adapt our language to address assistants such as Siri, Google or Alexa because they only understand certain things formulated in a specific way, in other words, rather than teach machines to be more human, we end up being more machine when addressing them. I'm not sure whether this is a good thing or a bad thing but I wouldn't be surprised if it had consequences on language evolution in the long term.
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Matt
Yasss, this is my field! I'm currently writing my master's thesis right now.
One of the big problems that NLP faces these days is that there's a small number of very well represented languages that our systems are built for and around, but a ton of languages that just aren't that supported. The point you make about Sign Languages is a notable example, but it exists for tons of spoken languages too. Since most of our systems are built with machine learning, which inherits bias in the data, there's a major focus to spread out to other languages these days. I was glad to see you mentioned the problem of bias more generally too, because it's probably the biggest problem in machine learning generally.
Another big problem is NLU, or natural language understanding. It's something of a misnomer, because to date we haven't built a system that can be said to -understand- language like a human does. This is a major stumbling block to further applications, as any kind of dialog agent is going to need to be able to make sense of world information.
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Yasss, this is my field! I'm currently writing my master's thesis right now.
One of the big problems that NLP faces these days is that there's a small number of very well represented languages that our systems are built for and around, but a ton of languages that just aren't that supported. The point you make about Sign Languages is a notable example, but it exists for tons of spoken languages too. Since most of our systems are built with machine learning, which inherits bias in the data, there's a major focus to spread out to other languages these days. I was glad to see you mentioned the problem of bias more generally too, because it's probably the biggest problem in machine learning generally.
Another big problem is NLU, or natural language understanding. It's something of a misnomer, because to date we haven't built a system that can be said to -understand- language like a human does. This is a major stumbling block to further applications, as any kind of dialog agent is going to need to be able to make sense of world information.
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DougOfTheAntarctic
- Easy to teach a computer to play chess? Well, it's not THAT easy.
- The story about computers producing strings of -eeeee- because e is the most common letter in English sounds apocryphal.
- I don't think she gave gloves a fair shake. A glove could definitely detect arm and hand as well as finger motions. (OK, not facial expressions) There's no reason they would only be capable of transliterating 26 spelled out letters - they could detect many other gestures besides. I wouldn't dismiss the concept just because deaf people didn't have the idea. I do think it's a rather complex solution to the minor inconvenience of deaf people simply typing their words using a keyboard. (That's also one-way translation. So what)
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- Easy to teach a computer to play chess? Well, it's not THAT easy.
- The story about computers producing strings of -eeeee- because e is the most common letter in English sounds apocryphal.
- I don't think she gave gloves a fair shake. A glove could definitely detect arm and hand as well as finger motions. (OK, not facial expressions) There's no reason they would only be capable of transliterating 26 spelled out letters - they could detect many other gestures besides. I wouldn't dismiss the concept just because deaf people didn't have the idea. I do think it's a rather complex solution to the minor inconvenience of deaf people simply typing their words using a keyboard. (That's also one-way translation. So what)
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Agnieszka
I desperately need the citation on those 2000 of word languages being used in social media currently (also: what factors can you think of for the other approx. 5000 not being used? I came up with: lacking internet access in those communities, lacking a writing system in te language completely or lacking a specific language writing system from UNICODE, censorship, so close-knit communities of small, endangered languages, that there's no need to use it outside a certain area, where everybody know each other.
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I desperately need the citation on those 2000 of word languages being used in social media currently (also: what factors can you think of for the other approx. 5000 not being used? I came up with: lacking internet access in those communities, lacking a writing system in te language completely or lacking a specific language writing system from UNICODE, censorship, so close-knit communities of small, endangered languages, that there's no need to use it outside a certain area, where everybody know each other.
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Manikarnika
This episode seemed more invested in providing disclaimers about computational linguistics than actually explaining how it works, especially when compared with how much was put into explaining how language itself works in earlier episodes. Guess you guys didn't want to step on CC Computer Science's toes? Yet that series also didn't really delve much into this topic (which is huge in linguistics today.
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This episode seemed more invested in providing disclaimers about computational linguistics than actually explaining how it works, especially when compared with how much was put into explaining how language itself works in earlier episodes. Guess you guys didn't want to step on CC Computer Science's toes? Yet that series also didn't really delve much into this topic (which is huge in linguistics today.
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Kalistic
what about written sign language? sounds like signed speech needs to be translated to English first before being written. And lots of signs are closer to ideograms rather than letters that represent sounds? Maybe Chinese writing would be easier (but then do Chinese signed speech follows spoken chinese rules or altogether different)
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what about written sign language? sounds like signed speech needs to be translated to English first before being written. And lots of signs are closer to ideograms rather than letters that represent sounds? Maybe Chinese writing would be easier (but then do Chinese signed speech follows spoken chinese rules or altogether different)
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Jose
Rather than using gloves, signs may be recognized by cameras instead so facial expressions, arm distance and gestures are recognized. But this would involve training computers with tons of sign language videos. It would be hard to get videos for sign languages used by smaller communities and developing areas though.
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Rather than using gloves, signs may be recognized by cameras instead so facial expressions, arm distance and gestures are recognized. But this would involve training computers with tons of sign language videos. It would be hard to get videos for sign languages used by smaller communities and developing areas though.
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Chad
Seems like it would be easy enough to make a glove that detected motion as well and pair with a camera for the facial expression part, but if you are already using cameras, I would think you could get the signs straight from that too. Seems like a good Machine Learning topic if no one is doing it.
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Seems like it would be easy enough to make a glove that detected motion as well and pair with a camera for the facial expression part, but if you are already using cameras, I would think you could get the signs straight from that too. Seems like a good Machine Learning topic if no one is doing it.
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2sudonim
I'm just here to note that neural networks function NOTHING like actual neurons. They're the product of a compsci researcher misinterpreting a non-technical description of a theoretical understanding of neuron function given during a casual conversation in the 80s.
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I'm just here to note that neural networks function NOTHING like actual neurons. They're the product of a compsci researcher misinterpreting a non-technical description of a theoretical understanding of neuron function given during a casual conversation in the 80s.
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Daily
I love how machine learning problems (I think NLP would fall in this category) can be improved with more training data. Amazon Alexa seems to be improving the fastest because it has the most market share and therefore the most training data.
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I love how machine learning problems (I think NLP would fall in this category) can be improved with more training data. Amazon Alexa seems to be improving the fastest because it has the most market share and therefore the most training data.
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