Answers.Body: <p>This problem is very challenging since you need to evaluate the quality of the candidate answers.</p>
  
  <p>In question answering, there are common steps that you need to follow. To summarise you need first to find the sentence that can answer the question and then compose the final answer.</p>
  
  <p>In the first step you can measure the semantical similarity between Q and A,   this a first filter where you can use several deep learning methods. Also, you can define a threshold to validate if the pair QA are enough related.</p>
  
  <p>In the second step, you must extract the answer, if the answer is a fact you can use KB extraction, or if it is a summary, list you can use other DL methods. There is also the possibility that you must infer the answer, also suitable for DL methods.</p>
  
  <p>I suggest to check this paper:</p>
  
  <p><a href="https://arxiv.org/abs/1502.05698" rel="nofollow noreferrer">Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-16T16:27:09.707Z
Answers.LastActivityDate: 2018-08-16T16:27:09.707Z
Answers.UserDisplayName: andres
Body: <p>I'm working on a project related to machine Q&amp;A, using the SQuAD dataset. I've implemented a neural-net solution for finding answers in the provided context paragraph, but the system (obviously) struggles when given questions that are unanswerable from the context. It usually produces answers that are nonsensical and of the wrong entity type.</p>
  
  <p>Is there any existing research in telling whether or not a question is answerable using the info in a context paragraph? Or whether a generated answer is valid? I considered textual entailment but it doesn't seem to be exactly what I'm looking for (though maybe I'm wrong about that?)</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-02-17T10:41:45.400Z
Comments.Text: That isn't an easy problem since you'd need AI capable of making common sense deductions.
  For example:
  There was war. Were there any dead people and of what gender and age were they?
  Yes and they were mostly young men. Or
  John is older than Peter. Ann is younger than Peter.
  Is Ann younger than John?
  Yes. You need a puzzle solving program, dudd.
Comments.UserDisplayName: Tone Škoda
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T16:34:45.563Z
Comments.Text: Have you solved this problem yet?
Comments.UserDisplayName: Enes mahmut kulak
Tags: <neural-networks><machine-learning><classification><natural-language-processing>
Title: Methods to tell if a question can be answered from a paragraph
UserDisplayName: BriJia
ThreadText: # Methods to tell if a question can be answered from a paragraph
  
  (Asked by: username_0 on 2018-02-16)
  
  I'm working on a project related to machine Q&A, using the SQuAD dataset. I've implemented a neural-net solution for finding answers in the provided context paragraph, but the system (obviously) struggles when given questions that are unanswerable from the context. It usually produces answers that are nonsensical and of the wrong entity type.
  
  Is there any existing research in telling whether or not a question is answerable using the info in a context paragraph? Or whether a generated answer is valid? I considered textual entailment but it doesn't seem to be exactly what I'm looking for (though maybe I'm wrong about that?)
  
  **Tags:** neural-networks, machine-learning, classification, natural-language-processing
  
  > **Comments:**
  > **username_1**: That isn't an easy problem since you'd need AI capable of making common sense deductions. For example: There was war. Were there any dead people and of what gender and age were they? Yes and they were mostly young men. Or John is older than Peter. Ann is younger than Peter. Is Ann younger than John? Yes. You need a puzzle solving program, dudd.  
  > **username_2**: Have you solved this problem yet?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2018-08-16. Score: 1
  
  This problem is very challenging since you need to evaluate the quality of the candidate answers.
  
  In question answering, there are common steps that you need to follow. To summarise you need first to find the sentence that can answer the question and then compose the final answer.
  
  In the first step you can measure the semantical similarity between Q and A, this a first filter where you can use several deep learning methods. Also, you can define a threshold to validate if the pair QA are enough related.
  
  In the second step, you must extract the answer, if the answer is a fact you can use KB extraction, or if it is a summary, list you can use other DL methods. There is also the possibility that you must infer the answer, also suitable for DL methods.
  
  I suggest to check this paper:
  
  [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://arxiv.org/abs/1502.05698)
  
  
  ---
  
  