<commit_before># Sequence-to-Sequence Models in Tensorflow & Keras

This project is still very much evolving each day, but the core goals are:
* Recreate the results described in the paper, "A Neural Conversation Model" [TODO: link to paper], but with the Ubuntu Dialogue Corpus as the dataset.
* Explore various implementations and/or improvements to the standard sequence-to-sequence model described in "Sequence to Sequence Learning with Neural Networks."
* Create a cleaner user interface for tinkering with such models and with many dataset options.
* Make a fun chatbot that can learn across datasets and can remember more than just the previous response.
<commit_msg>Added more info to the readme.<commit_after># Conversation Models in Tensorflow

This project is still very much evolving each day, but the core goals are:
* Recreate the results described in the paper, [A Neural Conversation Model](https://arxiv.org/pdf/1506.05869.pdf).
* Create a cleaner user interface for tinkering with such models and using multiple datasets.
* Explore how [personalities of chatbots](https://arxiv.org/pdf/1603.06155.pdf) change when trained on different datasets, and methods for improving speaker consistency.
* Add support for "teacher mode": an interactive chat session where the user can tell the bot how well they're doing, and suggest better responses that the bot can learn from.

At present, the following have been (more or less) completed:

* Models:
    * Implement an attention-based embedding sequence-to-sequence model with the help of the tensorflow.contrib libraries.
    * Implement a simpler embedding sequence-to-sequence from "scratch" (minimal use of contrib).
* Datasets:
    * **WMT'15** : English-to-French translation.
    * **Ubuntu Dialogue Corpus**. Reformatted as single-turn to single-response pairs.



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