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A meta-learning approach to text emotion distribution learning from small sample

Load data

  • load_data.py

Embedding

  • bert.py: BERT embedding

  • doc2vec_linear.py: doc2vec embedding

  • doc2vec_tensor.py: tensor embedding

  • doc2vec_infersent.py: InferSent embedding

Task partition

  • cluster_doc2vec.py: doc2vec

  • cluster_random.py: random

  • cluster_tensor.py: tensor embedding

  • cluster_infersent.py: InferSent

Methods

  • cnn_joint.py: CNN

  • infersent_joint.py: InferSent or doc2vec

  • bert.py: BERT

  • maml.py: meta-training, eval_maml.py: meta-adaption

Experiment templates

  • cv_portion.py: cross validation

  • example.sh: example shell

Note

  • download LDLPackage-v1.2 and tensor_toolbox, which have their own licenses

  • download SemEval Task #14 dataset

  • the results of label distribution learning methods, e.g., PT-X, AA-X, SA-X, can be calculated with LDLPackage-v1.2/edl/cv_portion_ldl.m

  • grid search can be conducted by cv_grids.py and LDLPackage-v1.2/edl/cv_grid.m

  • a quick demo can be experimented with bash example.sh and LDLPackage-v1.2/edl/example.m

  • if you want to use a different word embedding, first use compress_wv.py to get a compressed word embedding file and put it in the corresponding path

  • some codes should be tuned a bit (set the right paths) to work, feel free to contact me if you have any question

  • running the BERT method needs bert-as-service

  • running the InferSent method needs InferSent

GNU GENERAL PUBLIC LICENSE