NLP text autoencoder that generates text in poetic meter












0












$begingroup$


I would like to create an NLP autoencoder that happens to only generate text that conforms to a poetic meter, for example 'iambic pentameter'. That is, the output should be a series of clauses which are 10 syllables long and are read aloud with the verbal stresses as 'duh-DUH duh-DUH duh-DUH duh-DUH duh-DUH'. Shakespeare and his contemporaries used this format for much of their poetry.



The only method I can think of is to collect lines of text which are in this format, and then train an autoencoder on these. Since the network only emits iambic pentameter while training, it should also emit iambic pentameter while predicting. Given a large enough corpus, this seems like it should work.



One problem is that this training only accepts iambic pentameter. To rewrite other text into IP, the training needs to include variations of text in IP and train (not IP sentence) to emit (IP sentence). Generating these variations is straightforward. This crosses the line from training "sentence embeddings" to training "thought embeddings".



Are there other ways to solve this problem? Is there a way to directly alter the sentence embedding space? For example, variational sampling works by applying a nonlinear transformation on the embedding space.



Note: the CMU Pronouncing Dictionary supplies pronunciation stresses for over 100k words, usable for classifying meter:
http://www.speech.cs.cmu.edu/cgi-bin/cmudict



Note: this is a personal hobby project to teach myself deep learning & NLP- I really do not know whether it is achievable with current NLP technology.










share|improve this question









$endgroup$

















    0












    $begingroup$


    I would like to create an NLP autoencoder that happens to only generate text that conforms to a poetic meter, for example 'iambic pentameter'. That is, the output should be a series of clauses which are 10 syllables long and are read aloud with the verbal stresses as 'duh-DUH duh-DUH duh-DUH duh-DUH duh-DUH'. Shakespeare and his contemporaries used this format for much of their poetry.



    The only method I can think of is to collect lines of text which are in this format, and then train an autoencoder on these. Since the network only emits iambic pentameter while training, it should also emit iambic pentameter while predicting. Given a large enough corpus, this seems like it should work.



    One problem is that this training only accepts iambic pentameter. To rewrite other text into IP, the training needs to include variations of text in IP and train (not IP sentence) to emit (IP sentence). Generating these variations is straightforward. This crosses the line from training "sentence embeddings" to training "thought embeddings".



    Are there other ways to solve this problem? Is there a way to directly alter the sentence embedding space? For example, variational sampling works by applying a nonlinear transformation on the embedding space.



    Note: the CMU Pronouncing Dictionary supplies pronunciation stresses for over 100k words, usable for classifying meter:
    http://www.speech.cs.cmu.edu/cgi-bin/cmudict



    Note: this is a personal hobby project to teach myself deep learning & NLP- I really do not know whether it is achievable with current NLP technology.










    share|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      I would like to create an NLP autoencoder that happens to only generate text that conforms to a poetic meter, for example 'iambic pentameter'. That is, the output should be a series of clauses which are 10 syllables long and are read aloud with the verbal stresses as 'duh-DUH duh-DUH duh-DUH duh-DUH duh-DUH'. Shakespeare and his contemporaries used this format for much of their poetry.



      The only method I can think of is to collect lines of text which are in this format, and then train an autoencoder on these. Since the network only emits iambic pentameter while training, it should also emit iambic pentameter while predicting. Given a large enough corpus, this seems like it should work.



      One problem is that this training only accepts iambic pentameter. To rewrite other text into IP, the training needs to include variations of text in IP and train (not IP sentence) to emit (IP sentence). Generating these variations is straightforward. This crosses the line from training "sentence embeddings" to training "thought embeddings".



      Are there other ways to solve this problem? Is there a way to directly alter the sentence embedding space? For example, variational sampling works by applying a nonlinear transformation on the embedding space.



      Note: the CMU Pronouncing Dictionary supplies pronunciation stresses for over 100k words, usable for classifying meter:
      http://www.speech.cs.cmu.edu/cgi-bin/cmudict



      Note: this is a personal hobby project to teach myself deep learning & NLP- I really do not know whether it is achievable with current NLP technology.










      share|improve this question









      $endgroup$




      I would like to create an NLP autoencoder that happens to only generate text that conforms to a poetic meter, for example 'iambic pentameter'. That is, the output should be a series of clauses which are 10 syllables long and are read aloud with the verbal stresses as 'duh-DUH duh-DUH duh-DUH duh-DUH duh-DUH'. Shakespeare and his contemporaries used this format for much of their poetry.



      The only method I can think of is to collect lines of text which are in this format, and then train an autoencoder on these. Since the network only emits iambic pentameter while training, it should also emit iambic pentameter while predicting. Given a large enough corpus, this seems like it should work.



      One problem is that this training only accepts iambic pentameter. To rewrite other text into IP, the training needs to include variations of text in IP and train (not IP sentence) to emit (IP sentence). Generating these variations is straightforward. This crosses the line from training "sentence embeddings" to training "thought embeddings".



      Are there other ways to solve this problem? Is there a way to directly alter the sentence embedding space? For example, variational sampling works by applying a nonlinear transformation on the embedding space.



      Note: the CMU Pronouncing Dictionary supplies pronunciation stresses for over 100k words, usable for classifying meter:
      http://www.speech.cs.cmu.edu/cgi-bin/cmudict



      Note: this is a personal hobby project to teach myself deep learning & NLP- I really do not know whether it is achievable with current NLP technology.







      nlp






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 14 mins ago









      Jack ParsonsJack Parsons

      464




      464






















          0






          active

          oldest

          votes











          Your Answer





          StackExchange.ifUsing("editor", function () {
          return StackExchange.using("mathjaxEditing", function () {
          StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
          StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
          });
          });
          }, "mathjax-editing");

          StackExchange.ready(function() {
          var channelOptions = {
          tags: "".split(" "),
          id: "557"
          };
          initTagRenderer("".split(" "), "".split(" "), channelOptions);

          StackExchange.using("externalEditor", function() {
          // Have to fire editor after snippets, if snippets enabled
          if (StackExchange.settings.snippets.snippetsEnabled) {
          StackExchange.using("snippets", function() {
          createEditor();
          });
          }
          else {
          createEditor();
          }
          });

          function createEditor() {
          StackExchange.prepareEditor({
          heartbeatType: 'answer',
          autoActivateHeartbeat: false,
          convertImagesToLinks: false,
          noModals: true,
          showLowRepImageUploadWarning: true,
          reputationToPostImages: null,
          bindNavPrevention: true,
          postfix: "",
          imageUploader: {
          brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
          contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
          allowUrls: true
          },
          onDemand: true,
          discardSelector: ".discard-answer"
          ,immediatelyShowMarkdownHelp:true
          });


          }
          });














          draft saved

          draft discarded


















          StackExchange.ready(
          function () {
          StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f44981%2fnlp-text-autoencoder-that-generates-text-in-poetic-meter%23new-answer', 'question_page');
          }
          );

          Post as a guest















          Required, but never shown

























          0






          active

          oldest

          votes








          0






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes
















          draft saved

          draft discarded




















































          Thanks for contributing an answer to Data Science Stack Exchange!


          • Please be sure to answer the question. Provide details and share your research!

          But avoid



          • Asking for help, clarification, or responding to other answers.

          • Making statements based on opinion; back them up with references or personal experience.


          Use MathJax to format equations. MathJax reference.


          To learn more, see our tips on writing great answers.




          draft saved


          draft discarded














          StackExchange.ready(
          function () {
          StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f44981%2fnlp-text-autoencoder-that-generates-text-in-poetic-meter%23new-answer', 'question_page');
          }
          );

          Post as a guest















          Required, but never shown





















































          Required, but never shown














          Required, but never shown












          Required, but never shown







          Required, but never shown

































          Required, but never shown














          Required, but never shown












          Required, but never shown







          Required, but never shown







          Popular posts from this blog

          How to label and detect the document text images

          Vallis Paradisi

          Tabula Rosettana