SlowGuess/NAACL_2022_OCR huggingface dataset:
We claim that introducing imagination into language processing helps the existing language-based system tackle the low-resource situation. Thus, the automatically generated imagination helps reduce the human effort to annotate textual data. To verify this, we define two situations, a normal few-shot setting, and an extreme few-shot setting. For the normal few-shot setting, we keep  $1\%$ ,  $3\%$ , and  $5\%$  of the training dataset for each task in GLUE Benchmark. For the extreme few-shot setting, we keep a lower number of the training dataset, which is reduced to  $0.1\%$ ,  $0.3\%$ , and  $0.5\%$  of the training dataset. We train the models with the same configuration under these two settings and compare them with visually supervised transformer baselines to confirm the benefit that our proposed iACE brings to the few-shot situation.
Results of the few-shot setting are reported in Table 1. Following Tan and Bansal (2020), we only report the four largest and stable tasks in GLUE for the model-agnostic comparison. We report the accuracy for SST-2, QNLI, MNLI. For QQP and MRPC, we report the average of F1 and accuracy. For SWAG, we report the correlation. We observe that the imagination information remarkably helps with both the normal few-shot curriculum and extreme few-shot curriculum. We assume the imagination-augmented fine-tuning successfully transfers the language understanding from the large-scale vision and language model. Thus iACE achieves consistent performance gain and shows great superiority of generalization and transferring ability.
Base Model	Method	SST-2	QNLI	QQP	MNLI	ALL
0.1%	1.0%	3.0%	0.1%	1.0%	3.0%	0.1%	1.0%	3.0%	0.1%	1.0%	3.0%	Avg.
BERTbase	Direction	49.01	79.59	87.15	51.31	52.55	66.90	56.74	31.58	31.59	32.73	61.54	70.72	55.95
BERTbase	Unify	48.96	77.98	86.92	50.54	52.02	67.20	55.29	56.93	79.09	39.05	63.29	70.86	62.34
BERTbase	iACE	77.98	82.45	87.04	51.64	65.09	79.54	49.36	74.31	78.69	40.07	62.15	70.43	68.23
RoBERTabase	Direction	72.71	80.38	84.63	54.91	74.68	78.58	61.57	74.68	31.59	32.95	61.96	70.62	64.94
RoBERTabase	Unify	75.11	80.04	88.07	53.62	74.64	78.47	64.94	74.85	76.84	51.12	65.42	70.74	71.15
RoBERTabase	iACE	75.34	83.83	84.63	54.79	79.35	81.41	65.43	73.72	79.38	48.94	65.66	70.76	71.93
Table 2: Method Design Ablation in Few-shot Setting. We compare the results of two variants over  $0.1\%$ ,  $1.0\%$ ,  $3.0\%$  of SST-2, QNLI, QQP and MNLI dataset. Details of Direction and Unify are illustrated in Section 4.3.
Base Model	Composition	Extreme Few-shot (0.1%)	Normal Few-shot (3.0%)	ALL Avg.
SST-2	QNLI	QQP	MNLI	SST-2	QNLI	QQP	MNLI
BERTbase	Textual-Only	49.08	50.54	55.48	38.82	87.50	67.05	77.42	71.00	62.11
BERTbase	Visual-Only	59.97	50.56	49.01	39.05	86.81	67.23	79.06	70.80	62.81
BERTbase	Visual+Textual (VT)	53.89	50.54	49.15	38.83	87.04	66.81	79.16	70.77	62.02
BERTbase	Bi-directional VT	77.98	51.64	49.36	40.07	87.04	79.54	78.69	70.43	66.84
RoBERTabase	Textual-Only	75.57	53.85	64.96	35.28	84.07	78.51	75.76	51.48	64.93
RoBERTabase	Visual-Only	75.11	54.18	65.01	47.22	84.17	79.88	76.88	70.56	69.12
RoBERTabase	Visual+Textual (VT)	74.20	53.98	65.43	47.35	83.94	79.96	76.87	70.73	69.05
RoBERTabase	Bi-directional VT	75.34	54.79	65.43	48.94	84.63	81.41	79.38	70.76	70.08
Table 3: Imagination Composition Ablation in Few-shot Setting. Bi-directional VT represents the full input for iACE. More details about Textual Only, Visual Only and Visual+Textual are illustrated in Section 4.3.
We conduct ablation studies over both the method side and data side to validate their contribution to our proposed iACE.
Method Design Ablation Two method variants of our imagination-augmented encoder are built as baselines to validate the importance of our bi-directional cross-modal imagination design in iACE. The variants are built upon RoBERTa $_{base}$  and BERT $_{base}$  base models. Specifically, we develop variant Direction and Unify. Direction represent alignment between text input and imagination into a directional embedding as FUSE( $t_{sent1} - i_{sent1}$ ,  $t_{sent2} - i_{sent2}$ ). Unify encode the text and imagination, considering the direction from vision to language by encoding as FUSE( $t_{sent1}$ ,  $t_{sent2}$ ,  $i_{sent1}$ ,  $i_{sent2}$ ). While iACE consider direction from visoin to language and language to vision by encoding as the combination of FUSE( $t_{sent1}$ ,  $i_{sent2}$ ) and FUSE( $i_{sent1}$ ,  $t_{sent2}$ ). As shown in Table 2, our bi-directional imagination and language learning achieve stable and best average performance. These results indicate that our bi-directional imagination method design obtain generalization and transferring ability. We assume iACE benefits from both learning from language to vision and learning
from vision to language simultaneously.
Imagination Composition Ablation The composition of the imagination is essential for the performance. To further study the importance of full imagination, we ablate the data side by constructing a textual-only model denoted as Textual Only, a visual-only imagination denoted as Visual Only and a single directional imagination input denoted as Visual+Textual. Visual Only and Visual+Textual represent the imagination model use visual pairs  $(i_{sent1}, i_{sent2})$  and one direction visual and textual pairs  $(i_{sent1}, t_{sent2})$  as input respectively. Our full approach uses Bi-directional VT which takes  $(i_{sent1}, t_{sent2})$  and  $(t_{sent1}, i_{sent2})$  as input.
Results are reported in Table 3 for Extreme Few-shot setting and normal few-shot setting. We observe Bi-directional VT data input achieve the most stable and the best average performance. Results show the importance of bi-directional imagination from all the textual input to construct an imagination-augmented cross-modal encoder.
4.4 Model-agnostic Improvement
iACE is a model-agnostic training paradigm that could help existing models achieve consistent gain over GLUE and SWAG with both the few-shot
Base Model	Method	SST-2	QNLI	QQP	MNLI	MRPC	STS-B	SWAG	Avg.
BERTsmall	VOKEN	89.7	85.0	87.3	78.6	78.2	80.4	57.6	79.5
BERTsmall	iACE	89.8	86.2	87.7	78.9	78.4	82.7	57.9	80.2
BERTbase	VOKEN	92.2	88.6	88.6	82.6	83.5	86.0	70.6	84.6
BERTbase	iACE	91.7	88.6	89.1	82.8	85.8	86.6	70.8	85.1
RoBERTasmall	VOKEN	87.8	85.1	85.3	76.5	78.5	78.6	53.6	77.9
RoBERTasmall	iACE	89.2	85.1	86.5	76.8	79.0	78.7	53.7	78.3
RoBERTabase	VOKEN	90.5	89.2	87.8	81.0	87.0	86.9	68.5	84.4
RoBERTabase	iACE	91.6	89.1	87.9	82.6	87.7	86.9	68.5	84.9
Table 4: Model-agnostic Improvement in Full Data Setting. Results of iACE and VOKEN upon BERT and RoBERTa of small(6L/512H) and base(12L/768H) architecture are reported. The models are fine-tuned over GLUE Benchmark and SWAG with access to the full dataset. BEST results are highlighted.
(a) STS-B
(b) SNLI
Figure 4: Case studies on the STS-B and SNLI tasks. The baseline models yield predictions solely based on the text input, while our approach takes both the text input and corresponding visualization into consideration. On both tasks, our iACE gives predictions that are more aligned with the ground truth.
setting and full data setting. To validate such model-agnostic effectiveness of our proposed novel paradigm in processing natural language, we compare the performance with two language models (BERT and RoBERTa) of two architectures ("6L/512H" and "12L/768H"), and a strong visually supervised pre-trained baseline VOKEN (Tan and Bansal, 2020).
Table 4 shows the metric comparison on GLUE and SWAG. The base models are trained with a masked language model. The VOKEN model is pre-trained with a masked language model with an additional voken-classification task as introduced visual supervision. iACE achieves model-agnostic improvement over the model that solely fine-tune based on textual information, including the pure-language-based model and visually supervised pretrained model. The gain is consistently observed from different architectures of models.
Figure 4 lists out our examples for the case study. We show the results from the natural language inference and sentence similarity task. We use examples from the STS-B and SNLI datasets. Our contextual imagination describes the textual input as expected
and provides an external prediction reference.
For example (a), given the structurally diversified sentence and low  $n$ -grams overlaps but high semantic similarity, we observe that the pure language-based model predicts the wrong label. While the imagination helps the model capture the semantic similarity between two textual inputs via comparing the cross-modal semantics with the imagination information. From example (b), we observe the pure language-based model predicts the wrong label based on the similar sentence structure and high  $n$ -grams overlaps. While the imagination helps the model capture the difference between the similar premise and hypothesis text.
We treat the text-only learning problem in Natural Language Understanding tasks as a cross-modal language understanding problem with generated imagination as supervision. In this scenario, the task aims to bridge the gap between the human and the agent language understanding in both linguistic and perceptual procedures. To address the proposed problem, we devised a model-agnostic learning paradigm iACE. Specifically, we build the imagination of the downstream dataset using
an interactive generative approach with guidance from a self-supervised pre-trained large-scale image and text model. Our proposed iACE surpassed baselines of two architecture sizes by a large margin in the few-shot setting. The improvement is consistently observed over pure-language baselines (BERT and RoBERTa) and visually supervised VOKEN on the GLUE and SWAG dataset. The results show the superiority of our iACE in language understanding and handling low-resource circumstances.
We would like to thank the Robert N. Noyce Trust for their generous gift to the University of California via the Noyce Initiative. The research was also sponsored by the U.S. Army Research Office and was accomplished under Contract Number W911NF19-D-0001 for the Institute for Collaborative Biotechnologies. The writers' opinions and conclusions in this publication are their own and should not be construed as representing the sponsors or the U.S. government's official policy, expressed or inferred. Regardless of any copyright notation herein, the United States government is authorized to reproduce and distribute reprints for government purposes.
In this study, we only cover NLU datasets with English annotations. Such limitation is since the large-scale pre-trained multimodal models used in our studies, such as CLIP and VQGAN, are only trained on English corpus as of the date we conduct the experiments<sup>2</sup>.
This study use CLIP and VQGAN to render images given the text prompt. Suppose there exists any bias in the training dataset for the large-scale pre-trained multimodal models used in our study. In that case, our "imagination" approach may face an issue of fairness since the visual generative model might be more likely to illustrate specific types of images that it has seen in the training data. Moreover, if the training dataset for CLIP or VQGAN contains any personal information, then our "imagination" approach may strike a threat on privacy leakage given certain triggers or prompts. Even though we did not witness such issues in our study, we should keep in mind that the aforementioned behaviors would impair iACE's effectiveness.
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