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I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the s

I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the speaker's) at the time it's spoken. Also, I thought that the argument about the impossibility of distinguishing between equally probable meanings (in terms of frequency in a corpus) was a good one.

darawk 5y ago
> I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the speaker's) at the time it's spoken. While this is closer to a coherent point, i'm not sure it's particularly relevant to the idea of "natural language understanding". We consider humans to be understanding of natural language, but humans interpreting the words of other humans are constantly confounded by various forms of ambiguity or polysemy. The idea that you don't have NLU until you can perfectly ascertain the exact singular meaning of an utterance in the mind of the speaker implies that no human has achieved NLU either. > Also, I thought that the argument about the impossibility of distinguishing between equally probable meanings (in terms of frequency in a corpus) was a good one. It's certainly a good puzzle to pose for model building, which implies statistical models will need to be a bit more nuanced than simple word frequency correlations. But it certainly isn't impossible to assign latent attributes to words, and study the statistical properties of those attribute relations, which is exactly what would be necessary to solve their puzzle. In fact, modern word vectorization techniques (i.e. language models using lstms to predict the missing word in a sequence using adjacent context) essentially do this.
- silent_cal 5y ago
  1. Well said, but I would press it further and say that there is at least a pathway for us (human beings) to determine what the actual meaning of the utterance is by considering the context, the speaker, by asking for clarification, and so forth. The problem for the probabilistic/empirical language model is that it can't ever recognize "one right answer", even in theory. That's a problem, because there really is one discoverable meaning to every intentional utterance, even if it's not always successfully understood in practice. 2. How would you go about assigning latent attributes to words?
  - darawk 5y ago
    1. Yes, that's true. However i'm not sure I see clearly the distinction you're trying to draw here. You could build a neural network that can ask clarifying questions too. You can certainly define an ML model that recognizes one right answer as well: just pick the answer that has highest probability in your softmax output. 2. Well, there are a lot of ways to do this in principle, but word vectors in their original form are themselves latent attributes. All modern NLP is based on this concept already. Let's look at the 'suitcase' example in the article: > The trophy did not fit in the suitcase because it was too > 1a. small > 1b. big In NLP this problem is generally called "coreference resolution". That is, resolving which prior object a given pronoun is referring to. The ambiguity of this problem is indeed very subtle, and probably quite hard for an ML algorithm to learn. You can look at a parse example here: https://huggingface.co/coref/ https://huggingface.co/coref/ This is a near state of the art coreference resolution model, and it indeed fails to properly resolve here. Although you do see that the probabilities do actually shift towards the right answer, which is (very weakly) suggestive that it might be beginning to learn it. So, trying to unpack what's going on here linguistically, the essence of the problem is that the relation between "it" and "trophy" vs "suitcase" is contextually contingent. The necessary context is that trophies go inside of suitcases, and not vice versa. You then need to understand how the concept of insertion relates to sizes. Putting thing A inside of thing B requires that thing B be larger than thing A. This is certainly a subtle, context-rich problem. In order to try to solve this problem, a model would need to have a more structural understanding of language. You indeed cannot learn this problem based on simple word frequency counting, for exactly the reasons they state. However, even though the frequencies of big/small may be equal, there are more nuanced conditional probabilities that are not. Conditional on the latent concept of insertion, the pattern relating object size to coreference should be very statistically apparent.
    - silent_cal 5y ago
      1. I'm thinking of it in terms of two different kinds of knowledge that we're going after when we try to (A) understand a proposition vs (B) make a probabilistic guess at the right answer from among a discrete range of possibilities. However our understanding of language works, it's not the same as flagging a maximized probability. If someone says "the bank is on Fifth Street next to the fire hydrant", you immediately know all kinds of things when you take this in - that you're getting directions, that you will soon be looking for a fire hydrant, the address of the bank, that the speaker knows the area pretty well... this big "bundle of truth" emerges from the proposition immediately when we take it in. I just don't see it as analogous to flagging the highest softmax probability score, which at best provides a "most likely" translation into SQL code or a vector, or something like that. 2. If we're introducing concepts like insertion into the model, aren't we then returning to a logical (as opposed to a probabilistic/statistical) language model? Or said differently, aren't we merely introducing our own understanding of language into the model before it's even trained?

darawk 5y ago

> I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the speaker's) at the time it's spoken. While this is closer to a coherent point, i'm not sure it's particularly relevant to the idea of "natural language understanding". We consider humans to be understanding of natural language, but humans interpreting the words of other humans are constantly confounded by various forms of ambiguity or polysemy. The idea that you don't have NLU until you can perfectly ascertain the exact singular meaning of an utterance in the mind of the speaker implies that no human has achieved NLU either. > Also, I thought that the argument about the impossibility of distinguishing between equally probable meanings (in terms of frequency in a corpus) was a good one. It's certainly a good puzzle to pose for model building, which implies statistical models will need to be a bit more nuanced than simple word frequency correlations. But it certainly isn't impossible to assign latent attributes to words, and study the statistical properties of those attribute relations, which is exactly what would be necessary to solve their puzzle. In fact, modern word vectorization techniques (i.e. language models using lstms to predict the missing word in a sequence using adjacent context) essentially do this.

silent_cal 5y ago
1. Well said, but I would press it further and say that there is at least a pathway for us (human beings) to determine what the actual meaning of the utterance is by considering the context, the speaker, by asking for clarification, and so forth. The problem for the probabilistic/empirical language model is that it can't ever recognize "one right answer", even in theory. That's a problem, because there really is one discoverable meaning to every intentional utterance, even if it's not always successfully understood in practice. 2. How would you go about assigning latent attributes to words?
- darawk 5y ago
  1. Yes, that's true. However i'm not sure I see clearly the distinction you're trying to draw here. You could build a neural network that can ask clarifying questions too. You can certainly define an ML model that recognizes one right answer as well: just pick the answer that has highest probability in your softmax output. 2. Well, there are a lot of ways to do this in principle, but word vectors in their original form are themselves latent attributes. All modern NLP is based on this concept already. Let's look at the 'suitcase' example in the article: > The trophy did not fit in the suitcase because it was too > 1a. small > 1b. big In NLP this problem is generally called "coreference resolution". That is, resolving which prior object a given pronoun is referring to. The ambiguity of this problem is indeed very subtle, and probably quite hard for an ML algorithm to learn. You can look at a parse example here: https://huggingface.co/coref/ https://huggingface.co/coref/ This is a near state of the art coreference resolution model, and it indeed fails to properly resolve here. Although you do see that the probabilities do actually shift towards the right answer, which is (very weakly) suggestive that it might be beginning to learn it. So, trying to unpack what's going on here linguistically, the essence of the problem is that the relation between "it" and "trophy" vs "suitcase" is contextually contingent. The necessary context is that trophies go inside of suitcases, and not vice versa. You then need to understand how the concept of insertion relates to sizes. Putting thing A inside of thing B requires that thing B be larger than thing A. This is certainly a subtle, context-rich problem. In order to try to solve this problem, a model would need to have a more structural understanding of language. You indeed cannot learn this problem based on simple word frequency counting, for exactly the reasons they state. However, even though the frequencies of big/small may be equal, there are more nuanced conditional probabilities that are not. Conditional on the latent concept of insertion, the pattern relating object size to coreference should be very statistically apparent.
  - silent_cal 5y ago
    1. I'm thinking of it in terms of two different kinds of knowledge that we're going after when we try to (A) understand a proposition vs (B) make a probabilistic guess at the right answer from among a discrete range of possibilities. However our understanding of language works, it's not the same as flagging a maximized probability. If someone says "the bank is on Fifth Street next to the fire hydrant", you immediately know all kinds of things when you take this in - that you're getting directions, that you will soon be looking for a fire hydrant, the address of the bank, that the speaker knows the area pretty well... this big "bundle of truth" emerges from the proposition immediately when we take it in. I just don't see it as analogous to flagging the highest softmax probability score, which at best provides a "most likely" translation into SQL code or a vector, or something like that. 2. If we're introducing concepts like insertion into the model, aren't we then returning to a logical (as opposed to a probabilistic/statistical) language model? Or said differently, aren't we merely introducing our own understanding of language into the model before it's even trained?