Han Guo, Ramakanth Pasunuru, and Mohit Bansal. NAACL 2019 pdf

• The project originally runs in Tensorflow 1.8, but should be compatible for future versions (except TF 2.0).
• Python 3.5
• See requirements.txt

Download the data from GLUE, and follow the pre-processing from authors. A copy of the download script is provided in this repo. python download_glue_data.py --data_dir glue_data --tasks all

To compute the ELMo representations, use either TF-Hub or AllenNLP.

elmo = hub.Module("https://tfhub.dev/google/elmo/2", trainable=True) embeddings = elmo(..., as_dict=True)["elmo"]

AllenNLP's website includes a very detailed tutorial.

python run_MTL.py --logdir [logdir] --tasks [tasks] --embedding_dim [embedding_dim] --num_units [num_units] --num_layers [num_layers] --dropout_rate [dropout_rate] --learning_rate [learning_rate] --stage [stage]

python run_MTL.py --logdir [logdir] --tasks [tasks] --embedding_dim [embedding_dim] --num_units [num_units] --num_layers [num_layers] --dropout_rate [dropout_rate] --learning_rate [learning_rate] --stage [stage]

Pre-trained Models: append the ckpt_file argument to the command line arguments.

@inproceedings{guo2019autosem,
  title={AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning},
  author={Han Guo and Ramakanth Pasunuru and Mohit Bansal},
  booktitle={Proc. of NAACL},
  year={2019}
}