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Model description

This is the T5-3B model for System 3 DREAM-FLUTE (consequence), as described in our paper Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE, FigLang workshop @ EMNLP 2022 (Arxiv link: https://arxiv.org/abs/2210.16407)

Systems 3: DREAM-FLUTE - Providing DREAM’s different dimensions as input context

We adapt DREAM’s scene elaborations (Gu et al., 2022) for the figurative language understanding NLI task by using the DREAM model to generate elaborations for the premise and hypothesis separately. This allows us to investigate if similarities or differences in the scene elaborations for the premise and hypothesis will provide useful signals for entailment/contradiction label prediction and improving explanation quality. The input-output format is:

Input <Premise> <Premise-elaboration-from-DREAM> <Hypothesis> <Hypothesis-elaboration-from-DREAM>
Output <Label> <Explanation>

where the scene elaboration dimensions from DREAM are: consequence, emotion, motivation, and social norm. We also consider a system incorporating all these dimensions as additional context.

In this model, DREAM-FLUTE (consequence), we use elaborations along the "likely consequence" dimension. For more details on DREAM, please refer to DREAM: Improving Situational QA by First Elaborating the Situation, NAACL 2022 (Arxiv link: https://arxiv.org/abs/2112.08656, ACL Anthology link: https://aclanthology.org/2022.naacl-main.82/).

How to use this model?

We provide a quick example of how you can try out DREAM-FLUTE (consequence) in our paper with just a few lines of code:

>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/System3_DREAM_FLUTE_consequence_FigLang2022")

>>> tokenizer = AutoTokenizer.from_pretrained("t5-3b")
>>> input_string = "Premise: My decision-making skills are not purely based on emotions and gut. [Premise - likely consequence] I make more balanced and informed decisions. Hypothesis: My personal feelings color my judgment in this case. [Hypothesis - likely consequence] I make a decision that is not in the best interests of the company. Is there a contradiction or entailment between the premise and hypothesis?"
>>> input_ids = tokenizer.encode(input_string, return_tensors="pt")
>>> output = model.generate(input_ids, max_length=200)
>>> tokenizer.batch_decode(output, skip_special_tokens=True)
["Answer : Contradiction. Explanation : To have personal feelings color one's judgment means to make decisions based on them, but this context describes making decisions based on facts and not emotions"]

More details about DREAM-FLUTE ...

For more details about DREAM-FLUTE, please refer to our:

This model is part of our DREAM-series of works. This is a line of research where we make use of scene elaboration for building a "mental model" of situation given in text. Check out our GitHub Repo for more!

More details about this model ...

Training and evaluation data

We use the FLUTE dataset for the FigLang2022SharedTask (https://huggingface.co/datasets/ColumbiaNLP/FLUTE) for training this model. ∼7500 samples are provided as the training set. We used a 80-20 split to create our own training (6027 samples) and validation (1507 samples) partitions on which we build our models. For details on how we make use of the training data provided in the FigLang2022 shared task, please refer to https://github.com/allenai/dream/blob/main/FigLang2022SharedTask/Process_Data_Train_Dev_split.ipynb.

Model details

This model is a fine-tuned version of t5-3b.

It achieves the following results on the evaluation set:

  • Loss: 0.7505
  • Rouge1: 58.425
  • Rouge2: 38.2333
  • Rougel: 52.1326
  • Rougelsum: 52.1316
  • Gen Len: 41.0909

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • total_train_batch_size: 2
  • total_eval_batch_size: 2
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
0.9958 0.33 1000 0.8928 39.7038 27.4256 38.1226 38.1237 19.0
0.8973 0.66 2000 0.8252 41.4862 29.5302 39.6228 39.5913 18.9987
0.8837 1.0 3000 0.7854 41.2109 29.7022 39.6115 39.5989 19.0
0.5656 1.33 4000 0.8016 41.0368 29.76 39.4324 39.4341 19.0
0.5598 1.66 5000 0.7802 41.6073 30.3183 39.9937 39.9743 19.0
0.5495 1.99 6000 0.7505 41.7965 30.6031 40.1514 40.1509 19.0
0.3341 2.32 7000 0.8518 41.6758 30.9028 40.134 40.1415 18.9954
0.3493 2.65 8000 0.8544 41.5856 31.1526 40.154 40.1726 18.9940
0.3535 2.99 9000 0.8291 41.9552 31.4885 40.5239 40.5235 19.0

Framework versions

  • Transformers 4.22.0.dev0
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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