NLP - Predict most appropriate answer from Utterance












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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.









share









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    0












    $begingroup$


    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.









    share









    $endgroup$















      0












      0








      0





      $begingroup$


      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.









      share









      $endgroup$




      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.







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      nolw38nolw38

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