allenai/System3_DREAM_FLUTE_consequence_FigLang2022

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:

• 📄Paper: https://arxiv.org/abs/2210.16407
• 💻GitHub Repo: https://github.com/allenai/dream/

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