The principle of LM deep model The Next CEO of Stack Overflow2019 Community Moderator ElectionN-grams in NLP deep learningWhat principle is behind semantic segmenation with CNNs?How do we pass data to a RNN?what machine/deep learning/ nlp techniques are used to classify a given words as name, mobile number, address, email, state, county, city etcText Classification with deep learningEncoder-Decoder Sequence-to-Sequence Model for Translations in Both DirectionsWhy ELMo's word embedding can represent the word better than glove?Build an Autocomplete model for document titlesHow to train neural word embeddings?What is the difference between TextGAN and LM for text generation?
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The principle of LM deep model
The Next CEO of Stack Overflow2019 Community Moderator ElectionN-grams in NLP deep learningWhat principle is behind semantic segmenation with CNNs?How do we pass data to a RNN?what machine/deep learning/ nlp techniques are used to classify a given words as name, mobile number, address, email, state, county, city etcText Classification with deep learningEncoder-Decoder Sequence-to-Sequence Model for Translations in Both DirectionsWhy ELMo's word embedding can represent the word better than glove?Build an Autocomplete model for document titlesHow to train neural word embeddings?What is the difference between TextGAN and LM for text generation?
$begingroup$
Language model(LM) is the task of predicting the next word.
Does the deep model need the encoder? From the ptb code of tensor2tensor, I find the deep model do not contains the encoder.
Or both with-encoder and without-encoder can do the LM task?
neural-network deep-learning natural-language-process language-model transformer
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
$endgroup$
add a comment |
$begingroup$
Language model(LM) is the task of predicting the next word.
Does the deep model need the encoder? From the ptb code of tensor2tensor, I find the deep model do not contains the encoder.
Or both with-encoder and without-encoder can do the LM task?
neural-network deep-learning natural-language-process language-model transformer
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
$endgroup$
add a comment |
$begingroup$
Language model(LM) is the task of predicting the next word.
Does the deep model need the encoder? From the ptb code of tensor2tensor, I find the deep model do not contains the encoder.
Or both with-encoder and without-encoder can do the LM task?
neural-network deep-learning natural-language-process language-model transformer
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
$endgroup$
Language model(LM) is the task of predicting the next word.
Does the deep model need the encoder? From the ptb code of tensor2tensor, I find the deep model do not contains the encoder.
Or both with-encoder and without-encoder can do the LM task?
neural-network deep-learning natural-language-process language-model transformer
neural-network deep-learning natural-language-process language-model transformer
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
asked Mar 22 at 9:35
不是phd的phd不是phd的phd
2049
2049
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
The goal of LM is to learn a probability distribution over sequences
of symbols pertaining to a language.
That is, to learn $P(w_1,...,w_N)$ (resource).
This modeling can be accomplished by
- Predicting the next word given the previous words: $P(w_i | w_1,...,w_i-1)$, or
- Predicting the neighbor words given the center word (Skip-gram): $P(w_i+k| w_i), k in -2, -1, 1, 2$, or
- Predicting the center word given the neighbor words (CBOW or Continuous Bag-of-Words): $P(w_i| w_i-2,w_i-1,w_i+1, w_i+2)$, or other designs.
Does the deep model need the encoder? From the ptb code of
tensor2tensor, I find the deep model do not contains the encoder.
Yes. Modern LM solutions (all deep ones) try to find an encoding (embedding) that helps them to predict the next, neighbor, or center words as close as possible. However, a word encoding can be used as a constant input to other models. The ptb.py code calls text_encoder.TokenTextEncoder to receive such word encodings.
Both with-encoder and without-encoder can do the LM task?
LM task can be tackled without encoders too. For example, we can use frequency tables of adjacent words to build a model (n-gram modeling); e.g. all pairs (We, ?) appeared 10K times, pair (We, can) appeared 100 times, so P(can | We) = 0.01. However, encoder is the core of modern LM solutions.
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
$endgroup$
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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active
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votes
$begingroup$
The goal of LM is to learn a probability distribution over sequences
of symbols pertaining to a language.
That is, to learn $P(w_1,...,w_N)$ (resource).
This modeling can be accomplished by
- Predicting the next word given the previous words: $P(w_i | w_1,...,w_i-1)$, or
- Predicting the neighbor words given the center word (Skip-gram): $P(w_i+k| w_i), k in -2, -1, 1, 2$, or
- Predicting the center word given the neighbor words (CBOW or Continuous Bag-of-Words): $P(w_i| w_i-2,w_i-1,w_i+1, w_i+2)$, or other designs.
Does the deep model need the encoder? From the ptb code of
tensor2tensor, I find the deep model do not contains the encoder.
Yes. Modern LM solutions (all deep ones) try to find an encoding (embedding) that helps them to predict the next, neighbor, or center words as close as possible. However, a word encoding can be used as a constant input to other models. The ptb.py code calls text_encoder.TokenTextEncoder to receive such word encodings.
Both with-encoder and without-encoder can do the LM task?
LM task can be tackled without encoders too. For example, we can use frequency tables of adjacent words to build a model (n-gram modeling); e.g. all pairs (We, ?) appeared 10K times, pair (We, can) appeared 100 times, so P(can | We) = 0.01. However, encoder is the core of modern LM solutions.
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
$endgroup$
add a comment |
$begingroup$
The goal of LM is to learn a probability distribution over sequences
of symbols pertaining to a language.
That is, to learn $P(w_1,...,w_N)$ (resource).
This modeling can be accomplished by
- Predicting the next word given the previous words: $P(w_i | w_1,...,w_i-1)$, or
- Predicting the neighbor words given the center word (Skip-gram): $P(w_i+k| w_i), k in -2, -1, 1, 2$, or
- Predicting the center word given the neighbor words (CBOW or Continuous Bag-of-Words): $P(w_i| w_i-2,w_i-1,w_i+1, w_i+2)$, or other designs.
Does the deep model need the encoder? From the ptb code of
tensor2tensor, I find the deep model do not contains the encoder.
Yes. Modern LM solutions (all deep ones) try to find an encoding (embedding) that helps them to predict the next, neighbor, or center words as close as possible. However, a word encoding can be used as a constant input to other models. The ptb.py code calls text_encoder.TokenTextEncoder to receive such word encodings.
Both with-encoder and without-encoder can do the LM task?
LM task can be tackled without encoders too. For example, we can use frequency tables of adjacent words to build a model (n-gram modeling); e.g. all pairs (We, ?) appeared 10K times, pair (We, can) appeared 100 times, so P(can | We) = 0.01. However, encoder is the core of modern LM solutions.
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
$endgroup$
add a comment |
$begingroup$
The goal of LM is to learn a probability distribution over sequences
of symbols pertaining to a language.
That is, to learn $P(w_1,...,w_N)$ (resource).
This modeling can be accomplished by
- Predicting the next word given the previous words: $P(w_i | w_1,...,w_i-1)$, or
- Predicting the neighbor words given the center word (Skip-gram): $P(w_i+k| w_i), k in -2, -1, 1, 2$, or
- Predicting the center word given the neighbor words (CBOW or Continuous Bag-of-Words): $P(w_i| w_i-2,w_i-1,w_i+1, w_i+2)$, or other designs.
Does the deep model need the encoder? From the ptb code of
tensor2tensor, I find the deep model do not contains the encoder.
Yes. Modern LM solutions (all deep ones) try to find an encoding (embedding) that helps them to predict the next, neighbor, or center words as close as possible. However, a word encoding can be used as a constant input to other models. The ptb.py code calls text_encoder.TokenTextEncoder to receive such word encodings.
Both with-encoder and without-encoder can do the LM task?
LM task can be tackled without encoders too. For example, we can use frequency tables of adjacent words to build a model (n-gram modeling); e.g. all pairs (We, ?) appeared 10K times, pair (We, can) appeared 100 times, so P(can | We) = 0.01. However, encoder is the core of modern LM solutions.
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
$endgroup$
The goal of LM is to learn a probability distribution over sequences
of symbols pertaining to a language.
That is, to learn $P(w_1,...,w_N)$ (resource).
This modeling can be accomplished by
- Predicting the next word given the previous words: $P(w_i | w_1,...,w_i-1)$, or
- Predicting the neighbor words given the center word (Skip-gram): $P(w_i+k| w_i), k in -2, -1, 1, 2$, or
- Predicting the center word given the neighbor words (CBOW or Continuous Bag-of-Words): $P(w_i| w_i-2,w_i-1,w_i+1, w_i+2)$, or other designs.
Does the deep model need the encoder? From the ptb code of
tensor2tensor, I find the deep model do not contains the encoder.
Yes. Modern LM solutions (all deep ones) try to find an encoding (embedding) that helps them to predict the next, neighbor, or center words as close as possible. However, a word encoding can be used as a constant input to other models. The ptb.py code calls text_encoder.TokenTextEncoder to receive such word encodings.
Both with-encoder and without-encoder can do the LM task?
LM task can be tackled without encoders too. For example, we can use frequency tables of adjacent words to build a model (n-gram modeling); e.g. all pairs (We, ?) appeared 10K times, pair (We, can) appeared 100 times, so P(can | We) = 0.01. However, encoder is the core of modern LM solutions.
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
edited Mar 22 at 14:40
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
answered Mar 22 at 11:27
EsmailianEsmailian
2,048218
2,048218
add a comment |
add a comment |
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