NLP - Retrieval-based model Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsAttributes extraction from unstructured product descriptionsHow to implement multi class classifier for a set of sentences?How to improve Vector Space Models with semantic similarity?Creating the optimal set of utterances to train a natural language processing engineDoc2vec to calculate cosine similarity - absolutely inaccurateText understanding and mappingWhat to do if training loss decreases but validation loss does not decrease?How to create clusters based on sentence similarity?Find all potential similar documents out of a list of documents using clusteringTraining NLP with multiple text input features
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NLP - Retrieval-based model
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsAttributes extraction from unstructured product descriptionsHow to implement multi class classifier for a set of sentences?How to improve Vector Space Models with semantic similarity?Creating the optimal set of utterances to train a natural language processing engineDoc2vec to calculate cosine similarity - absolutely inaccurateText understanding and mappingWhat to do if training loss decreases but validation loss does not decrease?How to create clusters based on sentence similarity?Find all potential similar documents out of a list of documents using clusteringTraining NLP with multiple text input features
$begingroup$
My goal is to predict the most appropriate answer from an utterance, in a group of 21 potential answers.
Example:
Utterance: How are you today?
Answers: Answer1, 2, ..., 21.
I have a training file with this format:
Utterance:
Answers: Good answer, wrong answer1, wrong answer2,..., wrong answer20.
My problem
For the first time, we have to make a prediction from a group of possible answers, and, thus, this is a MCQ form.
Any ideas how I could start the problem?
What I've done
For the moment, the only thing I did was to choose the answers from the 21 possible answers which had the highest cosine similarity with the utterance. (So, unsupervised). It's not that bad (24% against 1/21 at random), but I'm sure there are ways to make something really better.
What I don't want to do at first
Use a generative model which predicts a full sentence. I want to choose the best candidate amongs the 21 answers, and use the training file which can allow us to do supervised learning.
nlp chatbot
asked 7 mins ago
nolw38nolw38
63
$endgroup$
add a comment |
$begingroup$
My goal is to predict the most appropriate answer from an utterance, in a group of 21 potential answers.
Example:
Utterance: How are you today?
Answers: Answer1, 2, ..., 21.
I have a training file with this format:
Utterance:
Answers: Good answer, wrong answer1, wrong answer2,..., wrong answer20.
My problem
For the first time, we have to make a prediction from a group of possible answers, and, thus, this is a MCQ form.
Any ideas how I could start the problem?
What I've done
For the moment, the only thing I did was to choose the answers from the 21 possible answers which had the highest cosine similarity with the utterance. (So, unsupervised). It's not that bad (24% against 1/21 at random), but I'm sure there are ways to make something really better.
What I don't want to do at first
Use a generative model which predicts a full sentence. I want to choose the best candidate amongs the 21 answers, and use the training file which can allow us to do supervised learning.
nlp chatbot
asked 7 mins ago
nolw38nolw38
63
$endgroup$
add a comment |
$begingroup$
My goal is to predict the most appropriate answer from an utterance, in a group of 21 potential answers.
Example:
Utterance: How are you today?
Answers: Answer1, 2, ..., 21.
I have a training file with this format:
Utterance:
Answers: Good answer, wrong answer1, wrong answer2,..., wrong answer20.
My problem
For the first time, we have to make a prediction from a group of possible answers, and, thus, this is a MCQ form.
Any ideas how I could start the problem?
What I've done
For the moment, the only thing I did was to choose the answers from the 21 possible answers which had the highest cosine similarity with the utterance. (So, unsupervised). It's not that bad (24% against 1/21 at random), but I'm sure there are ways to make something really better.
What I don't want to do at first
Use a generative model which predicts a full sentence. I want to choose the best candidate amongs the 21 answers, and use the training file which can allow us to do supervised learning.
nlp chatbot
asked 7 mins ago
nolw38nolw38
63
$endgroup$
My goal is to predict the most appropriate answer from an utterance, in a group of 21 potential answers.
Example:
Utterance: How are you today?
Answers: Answer1, 2, ..., 21.
I have a training file with this format:
Utterance:
Answers: Good answer, wrong answer1, wrong answer2,..., wrong answer20.
My problem
For the first time, we have to make a prediction from a group of possible answers, and, thus, this is a MCQ form.
Any ideas how I could start the problem?
What I've done
For the moment, the only thing I did was to choose the answers from the 21 possible answers which had the highest cosine similarity with the utterance. (So, unsupervised). It's not that bad (24% against 1/21 at random), but I'm sure there are ways to make something really better.
What I don't want to do at first
Use a generative model which predicts a full sentence. I want to choose the best candidate amongs the 21 answers, and use the training file which can allow us to do supervised learning.
nlp chatbot
nlp chatbot
asked 7 mins ago
nolw38nolw38
63
asked 7 mins ago
nolw38nolw38
63
edited 1 min ago
nolw38
asked 7 mins ago
nolw38nolw38
63
asked 7 mins ago
nolw38nolw38
63
asked 7 mins ago
nolw38nolw38
63
63
add a comment |
add a comment |
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