Code for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090
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README.md

MojiTalk

Xianda Zhou, and William Yang Wang. 2018. Mojitalk: Generating emotional responses at scale. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1128–1137. Association for Computational Linguistics.

Paper: https://arxiv.org/abs/1711.04090

Our lab: http://nlp.cs.ucsb.edu/index.html

Emojis

The file emoji-test.txt (http://unicode.org/Public/emoji/5.0/emoji-test.txt) provides data for loading and testing emojis. The 64 emojis that we used in our work are marked with '64' in our modified emoji-test.txt file.

Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the U.S. and other countries.

For terms of use, see http://www.unicode.org/terms_of_use.html

Dependencies

  • Python 3.5.2
  • TensorFlow 1.2.1

Usage

  1. Preparation:

    Set up an environment according to the dependencies.

    Dataset: https://drive.google.com/file/d/1l0fAfxvoNZRviAMVLecPZvFZ0Qexr7yU/view?usp=sharing

    Unzip mojitalk_data.zip to the current path, creating mojitalk_data directory where our dataset is stored. Read the readme.txt in it for the format of the dataset.

  2. Base model:

    1. Set the is_seq2seq variable in the cvae_run.py to True

    2. Train, test and generate: python3 cvae_run.py

      This will save several breakpoints, a log file and generation output in mojitalk_data/seq2seq/<timestamp>/

  3. CVAE model:

    1. Set the is_seq2seq variable in the cvae_run.py to False

    2. Set path of pretrain model: Modify line 67 of cvae_run.py to load a previously trained base model. e.g.: saver.restore(sess, "seq2seq/07-17_05-49-50/breakpoints/at_step_18000.ckpt")

    3. Train, test and generate: python3 cvae_run.py

      This will save several breakpoints, a log file and generation output in mojitalk_data/cvae/<timestamp>/.

      Note that the choice of base model breakpoint as the pretrain setting would influence the result of CVAE training. A overfitted base model may cause the CVAE to diverge.

  4. Reinforced CVAE model:

    1. Train the emoji classifier: CUDA_VISIBLE_DEVICES=0 python3 classifier.py

      The trained model will be saved in mojitalk_data/classifier/<timestamp>/breakpoints as a tensorflow breakpoint.

    2. Set path of pretrain model: Modify line 63/74 of rl_run.py to load a previously trained CVAE model and the classifier.

    3. Train, test and generate: python3 rl_run.py

      This will save several breakpoints, a log file and generation output in mojitalk_data/cvae/<timestamp>/.