sustcsonglin / beamtreerecursivecells Goto Github PK

• framework, models/layers/geometric, models/layers/ndr_geometric_stack.py adapted from: https://github.com/RobertCsordas/ndr
• models/encoder/OrderedMemory.py adapted from: https://github.com/yikangshen/Ordered-Memory
• Gumbel-Tree, BT-GRC, and GT-GRC related models are adapted from: https://github.com/jihunchoi/unsupervised-treelstm
• optimizers/ranger.py adapted from: https://github.com/anoidgit/transformer/blob/master/optm/ranger.py
• models/topk_module.py taken from the supplementary code in: https://proceedings.neurips.cc/paper/2020/hash/ec24a54d62ce57ba93a531b460fa8d18-Abstract.html

• torch==1.10.0
• tqdm==4.62.3
• jsonlines==2.0.0
• torchtext==0.8.1
• ninja==1.10.2
• typing-extensions==4.5.0
• psutil==5.8.0

• Put the Logical Inference data files (train0,train1,train2,.....test12) (downloaded from https://github.com/yikangshen/Ordered-Memory/tree/master/data/propositionallogic) in data/proplogic/
• Download the ListOps data (along with extrapolation data) from the urls here: https://github.com/facebookresearch/latent-treelstm/blob/master/data/listops/external/urls.txt and put the tsv files in data/listops/
• Run all the make*.py files in data/listops/ to create relevant splits (exact splits used in the paper will be released later)
• Download LRA (https://github.com/google-research/long-range-arena) dataset
• From LRA dataset put the ListOps basic_test.tsv (LRA test set) in data/listops
• Download IMDB original split from here: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz, extract and put the acllmdb folder in data/IMDB/
• Download IMDB contrast set from here: https://github.com/allenai/contrast-sets/tree/main/IMDb/data, put the dev_original.tsv, dev_contrast.tsv and test_contrast.tsv in data/IMDB/
• Download IMDB counterfactual test set from here: https://github.com/acmi-lab/counterfactually-augmented-data/blob/master/sentiment/new/test.tsv; rename it to "test_counterfactual.tsv". Put it in data/IMDB
• Download MNLI datasets (https://cims.nyu.edu/~sbowman/multinli/) and put them in data/MNLI/
• Download MNLI stress tests (https://abhilasharavichander.github.io/NLI_StressTest/) and put them in data/MNLI/
• Put glove.840B.300d.txt (download glove.840B.300d.zip from here:https://nlp.stanford.edu/projects/glove/) in embeddings/glove/

You can verify if the data is properly set up from the directory tree here.

• Go to preprocess/ and run each preprocess files to preprocess the corresponding data (process_SNLI_addon.py must be run after process_SNLI.py; otherwise no order requirement)

We share some of the processed data with its exact splits here (put the processed_data folder in the outermost project directory).

Train: python trian.py --model=[insert model name] -- dataset=[insert dataset name] --times=[insert total runs] --device=[insert device name] --model_type=[classifier/sentence_pair]

• Check argparser.py for exact options.
• Model type sentence_pair represents sentence-matching tasks like NLI. Modely type classifier represents simple sentence classification tasks.
• Generally we use total times as 3.

• For tree parsing from a classifier model: python extract_trees_classifier.py --model=[insert model name] --device=[insert device name] -- dataset=[insert dataset name]
• For tree parsing from a sentence-paur matching model: python extract_trees_nli.py --model=[insert model name] --device=[insert device name] -- dataset=[insert dataset name]

Inputs for parsing can be modified from inside a list in extract_trees_classifier.py or python extract_trees_nli.py (line 66)

The dataset nomenclature in the codebase and in the paper are a bit different. We provide a mapping here of the form ([codebase dataset name] == [paper dataset name])

• listopsc == ListOps
• listopsd == ListOps-DG
• listops_ndr50 == ListOps-DG1
• listops_ndr100 == ListOps-DG2
• proplogic == Logical Inference (Operator generalization split)
• proplogic_C == Logical Inference (C-split for systematic generalization)
• SST2 == SST2
• SST5 == SST5
• IMDB == IMDB
• MNLIdev == MNLI

The speed-suffixed names are for stress tests.

The model nomenclature in the codebase and in the paper are a bit different. We provide a mapping here of the form ([codebase model name] == [paper model name])

• RCell == RecurrentGRC
• BalancedTreeCell == BalancedTreeGRC
• RandomTreeCell == RandomTreeGRC
• GoldTreeCell == GoldTreeGRC
• GumbelTreeLSTM == GumbelTreeLSTM
• GumbelTreeCell == GumbelTreeGRC
• MCGumbelTreeCell == MCGumbelTreeGRC
• CYKCell == CYK-GRC
• OrderedMemory = Ordered Memory
• CRvNN == CRvNN
• CRvNN_worst == CRvNN without halt (during stress test)
• BSRPCell == BSRP-GRC (beam 5)
• BigBSRPCell == BSRP-GRC (beam 8)
• NDR = NDR (Neural Data Router)
• BeamTreeLSTM == BT-LSTM (beam 5)
• BeamTreeCell == BT-GRC (beam 5)
• SmallerBeamTreeCell == BT-GRC (beam 2)
• DiffBeamTreeCell == BT-GRC + OneSoft (beam 5)
• SmallerDiffBeamTreeCell == BT-GRC + OneSoft (beam 2)
• DiffSortBeamTreeCell == BT-GRC + SOFT (beam 5)

@InProceedings{Chowdhury2023beam,
  title = 	 {Beam Tree Recursive Cells},
  author =       {Ray Chowdhury, Jishnu and Caragea, Cornelia},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  year = 	 {2023}
}

Contact the associated github email for any question or issue.