SlowGuess/NAACL_2022_OCR huggingface dataset:
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A.1 Computing Infrastructure
All experiments were performed on a single Tesla V100 GPU. The complete model  $(\mathbf{CD} + \mathbf{CA} + \mathbf{DND})$  takes around 10 hours for training with 32-bit precision on a single cross-validation fold with a batch size of 25.
We used a combination of randomized and manual search to tune the hyperparameters. For each cross fold, we kept 50 dialogues from the CD training data for parameter tuning. This amounts to 100 data points, considering the two perspectives extracted from each dialogue. The metric for choosing the best hyperparameters is EMA at  $k = 5$ , averaged over the 5 cross-validation folds. We tuned the parameters on the performance of the BERT-based model with  $\mathbf{CD} + \mathbf{CA} + \mathbf{DND}$  configuration.
We vary the learning rate in  $\{1e^{-5}, 2e^{-5}, 3e^{-5}\}$ , dropout in  $\{0.0, 0.1, 0.2\}$ , and loss-specific dropout in  $\{0.0, 0.15, 0.25\}$ . We also varied the number of transformer layers in Level II encoder from Figure 2 in the set  $\{1, 2, 3\}$ . For DND, we also varied the number of instances that were chosen for adaptation but found that using all the instances that passed our filtering gave the best performance. We further varied the margin for ranking loss in  $\{0.0, 0.3, 0.5\}$ . Finally, for the models trained on combined datasets, we tried with a higher weightage (2x) for the loss contribution of CA-adapted instances due to their lower total count but found no visible improvements in the performance. The rest of the hyper-parameters were fixed based on the available computational and space resources. We report the best performing hyper-parameters in the main paper.
The models used in the paper have nearly 171 million trainable parameters. We report the mean performance on the validation set in Table 3.
A.3 External Packages and Frameworks
The models were developed in PyTorch Lightning<sup>7</sup> and relied on the HuggingFace Transformers library<sup>8</sup> for using the pretrained models and their corresponding tokenizers. We used a number of
Model	EMA
Random	17.8 (4.87)
BoW-Ranker	35 (3.35)
Bert-based
DND	51 (1.67)
CA + DND	51.2 (3.12)
CD	63.6 (4.84)
CD + CA	65.8 (1.94)
CD + DND	69 (2.28)
CD + CA + DND	70 (2.61)
RoBerta-based
DND	54.6 (5.43)
CA + DND	55 (5.55)
CD	70.2 (3.19)
CD + CA	70 (3.95)
CD + DND	75.6 (2.15)
CD + CA + DND	77.8 (2.32)
Table 3: Validation performance for opponent modeling on CD dataset. The reported EMA is at  $k = 5$ . The numbers represent Mean (Std.) over 5-cross folds of the CD data.
external packages such as Python Scikit Learn $^{9}$  library for implementing the evaluation metrics, and NLTK $^{10}$  for tokenization for the Bag-of-Words model.
B Regular Expression Usage
B.1 Adapting DealOrNoDeal data
We randomly mapped book from DealOrNoDeal to food, replacing all occurrences of 'book' and 'books' with 'food' in the utterances. Similarly, hat was mapped to water, and ball was mapped to firewood. Since the dialogues only involve minimal context about the issues, we found these replacements to be sufficient.
B.2 Identifying Offer statements
The offer statements were also recognized by regular expressions for the purpose of computing average attention scores. Specifically, an utterance is classified as having an offer, if it contains 3 or more of the following phrases -  $\{0',1',2',3',one'\}$ , 'two', 'three', 'all the', 'food', 'water', 'firewood', 'i get', 'you get', 'what if', 'i take', 'you can take', 'can do'}. The threshold 3 and these phrases were chosen heuristically via qualitative analysis.
$^{7}$ https://www.pytorchlightning.ai/  
 $^{8}$ https://github.com/huggingface/transformers
$^{9}$ https://scikit-learn.org/stable/modules/model_evaluation.html  
 $^{10}$ https://www.nltk.org/api/nltktokenizerize.html
C Comparison with Human Performance
Figure 4: Mean performance comparison for the best performing model with the human expert for different values of  $k$ .
We present the performance for our best performing model with the human expert across different values of  $k$  in Figure 4.