SlowGuess/ACL_2020_OCR huggingface dataset:
Table 3: Automatic evaluation on the subsets of VisPro and VisDialConv dataset. We found history based MCA models to outperform significantly compared to the MCA-I model. On VisDialConv, MCA-I-VGH still outperform all other models in spare annotation phase while MCA-I-HGuidedQ performs the best after fine-tuning.
Annotation	Count	Percentage
VQA turns	594	67.12%
History required	97	10.96%
Common Sense	94	10.62%
Guess	59	6.67%
Cant tell	34	3.84%
Not relevant	7	0.79%
Table 4: Results of crowd-sourcing study to understand whether humans require dialog history to answer the question. 'VQA turns' indicate that humans could potentially answer correctly without having access to the previous conversation while 'History required' are the cases identified requiring dialog context. We also identified the cases requiring world knowledge/ common sense, guessing and questions not relevant to the image.
nal 1035 image-question pairs, leaving us with 885 examples. The results in Table 4 show that only  $11\%$  required actual dialog historical context according to the crowd-workers. Most of the time (67% cases), crowd-workers said they can answer the question correctly without requiring history.
The results in Table 3 are on the subset of 97 questions which the crowd-workers identified as requiring history. They show that history encoding models (MCA-I-HGuidedQ / MCA-I-HConcQ / MCA-I-H / MCA-I-VGH) significantly outperform MCA-I, suggesting that this data cannot be modelled as multiple rounds of VQA. It can also be seen that all the models with dense (early) interaction of the historical context outperform the one with late interaction (MCA-I-H) in terms of NDCG. Models with dense interactions appear to be more reliable in choosing other correct relevant answers because of the dialog context.
Our best performing model on VisDialConv is MCA-I-HGuidedQ and achieves a NDCG value of 62.9 after curriculum fine-tuning. However, on the VisPro subset, we observe that MCA-I-H still outperforms the other models. Interestingly, on this set, MCA-I also outperforms other history encoding models (except for MCA-I-H).
In sum, our analysis shows that only a small subset of the VisDial dataset contains questions which require dialog history, and for those, models which encode history lead to better results. We posit that this is due to the fact that questions with pleonastic pronouns such as "Is it sunny/daytime/day..." are the most frequent according to our detailed analysis in Appendix C about the dialog phenomena.
Relevance of GT	Train	Val
Count	Percent	Count	Percent
1	1057	52.85%	643	31.15%
0.8	-	-	397	19.23%
0.6	-	-	330	15.99%
0.5	526	26.30%	-	-
0.4	-	-	281	13.61%
0.2	-	-	227	11.00%
0	417	20.85%	186	9.01%
Total	2000	100%	2064	100%
Table 5: Relevance score (dense annotation) provided for  $2\mathrm{k} / 2\mathrm{k}$  train/val QA turns. We find that  $20\%$  of the ground truth answers were marked as irrelevant (0 score) and partially relevant (0.5 score) by the human annotators for train set. This can be attributed to human errors made while collecting the original data as well as when crowd-sourcing the dense annotations.
6.2 Dense Annotations for NDCG
Here, we investigate the discrepancy between the NDCG results and the other retrieval-based methods. First, we find that the annotation scales differs: while there is a 3-way annotation on the train set, the val set defines 6 possible relevance classes, see Table 5. This affects the evaluation results of our
<sup>8</sup>We took care to only include examples from Visdial val set in both Vispro and VisDialConv subsets. Also note, there are only 8 overlapping instances between Vispro and VisdialConv subsets.
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Image
A bag of chips and a apple and orange.  $N_{Rel}$  : 15
Dialog
MCA-I-H
MCA-I-VGH
A remote controller is hidden in a console inside of an arm rest.  $N_{Rel}$  :8
Figure 4: Top-5 ranked predictions (relevance in parentheses) of MCA-I-H and MCA-I-VGH after both sparse annotation and curriculum fine-tuning phase.  $R_{GT}$  defines the rank of Ground Truth (GT) predicted by the model. We also calculate NDCG of rankings for current question turn.  $N_{Rel}$  denotes number of candidate answer options (out of 100) with non-zero relevance (dense annotations). Here  $\spadesuit$  and  $\spadesuit$  represents predictions after sparse annotation and curriculum fine-tuning respectively.
model, for which we can't do much.
Next, a manual inspection reveals that the relevance weight annotations contain substantial noise: We find that ground truth answers were marked as irrelevant for about  $20\%$  of train and  $10\%$  of val set. Thus, our models seem to get "confused" by fine-tuning on this data. We, therefore, manually corrected the relevance of only these GT answers (in dense annotations of train set only, but not in val set). Please see Appendix D for further details. The results in Table 1 (for MCA-I-H-GT) show that the model fine-tuned on the corrected data still achieves a comparable NDCG result, but substantially improves stricter (single answer) metrics, which confirms our hypothesis.
Finally, due to the noisy signal they receive during fine-tuning, our models learn to select "safe" answers<sup>9</sup>, such as "I can't tell" (see examples in
Figure 4), which rank high according to (the more forgiving) NDCG, but perform poorly for stricter metrics like MRR and Recall.
Our results suggest that the VisDial dataset only contains very limited examples which require dialog history. Other visual dialog tasks, such as GuessWhich? (Chattopadhyay et al., 2017) and GuessWhat?! (De Vries et al., 2017) take place in a goal-oriented setting, which according to Schlangen (2019), will lead to data containing more natural dialog phenomena. However, there is very limited evidence that dialog history indeed matters for these tasks (Yang et al., 2019). As such, we see data collection to capture visual dialog phenomena as an open problem.
Nevertheless, our results also show that encoding dialog history still leads to improved results. This is in contrast with early findings that a) "naive" encoding will harm performance (Das et al. (2017);
at least  $13.3\%$  of answers are non-committal (I cannot tell, Not sure, I can't tell).
<sup>9</sup>We show the statistics of top-ranked predictions by our MCA-I-H model on our VisualConv subset (i.e. 97 dialogs of the Visual val set). Read as: (Response, count, %) (Yes, 14, 14%) (No, 11, 11.34%) (I cannot tell, 9, 9.27%) (Nope, 3, 3%) (Not that I see, 2, 2.06%) (Red and white, 2, 2.06%) (Not sure, 2, 2.06%) (I can't tell, 2, 2.06%). This shows that
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see MCA-I-HConcQ in Table 1), or that b) history is not necessary (Massiceti et al., 2018).
Furthermore, we find that our model learns to provide generic answers by taking advantage of the NDCG evaluation metric. Learning generic answers is a well-known problem for open-domain dialog systems, e.g. (Li et al., 2016). While the dialog community approaches these phenomena by e.g. learning better models of coherence (Xu et al., 2018), we believe that evaluation metrics also need to be improved for this task, as widely discussed for other generation tasks, e.g. (Liu et al., 2016; Novikova et al., 2017; Reiter, 2018). As a first step, BERT score (Zhang et al., 2019) could be explored to measure ground-truth similarity replacing the noisy NDCG annotations of semantic equivalence.
In sum, this paper shows that we can get SOTA performance on the VisDial task by using transformer-based models with Guided-Attention (Yu et al., 2019b), and by encoding dialog history and finetuning we can improve results even more.
Of course, we expect pre-trained visual BERT models to show even more improvements on this task, e.g. Vilbert (Lu et al., 2019), LXMert (Tan and Bansal, 2019), UNITER (Chen et al., 2019) etc. However, we also show the limitations of this shared task in terms of dialog phenomena and evaluation metrics. We, thus, argue that progress needs to be carefully measured by posing the right task in terms of dataset and evaluation procedure.
We thank the anonymous reviewers for their insightful comments. Shubham would like to thank Raghav Goyal for the discussions during 'Pikabot' submission to Visual Dialog Challenge 2018. This work received continued support by Adobe Research gift funding for further collaboration. This research also received funding from Adeptmind Inc., Toronto, Canada and the EPSRC project MaDrIgAL (EP/N017536/1). We would also like to acknowledge the AWS Cloud Credits for Research programme.
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We built our implementation upon starter code in PyTorch which the VisDial organisers kindly provided. $^{10}$  We follow the guidelines of Teney et al. (2018) and used static 36 as the number of object proposals in our experiments (though our model can handle dynamic number of proposals).
We experimentally determined the learning rates of 0.0005 for training MCA models and 0.0001 for fine-tuning and reducing it by 1/10 after every 7 and 10 epochs out of a total of 12 epochs for training and 1/5 after 2 epochs for fine-tuning.
We use pytorch's LambdaLR scheduler while training and ReduceLROnPlateau for the finetuning procedure. Dropout of 0.2 is used for regularization and we perform early stopping and saved the best model by tracking the NDCG value on val set. Layer normalisation (Ba et al., 2016) is used for stable training following (Vaswani et al., 2017; Yu et al., 2019b). Attention reduction consisted of 2 layer MLP (fc(d)-ReLU-Dropout(0.2)-fc(1)).
We also experimented with different contextual representations, including BERT (Devlin et al., 2019); However we didn't observe any improvement, similar to the observation by (Tan and Bansal, 2019).
For the results on the validation set, only the training split is used. To report results on test-standard set, both the training and val set are used for training. For curriculum fine-tuning we use multi-class cross entropy loss where weighted by the relevance score. All our MCA modules have 6 layers and 8 heads, which we determined via a hyper parameter search. Table 7 shows more details.
Annotation	Text
VQA turns	I can confidently tell the correct answer just seeing the image.
History required	I want to know what was discussed before to answer confidently. Cannot answer with just the question and image. Need more information (context) from previous conversation.
Common Sense	I can answer it but by inferring using common sense.
Guess	I can only guess the answer.
Cant tell	I can’t tell the answer.
Not relevant	Not relevant question for this image.
Table 6: Mapping of human annotation with the actual text shown to the user.
10https://github.com/batra-mlp-lab/ visdial-challenge-starter-pytorch.
Model	Training	Curriculum Fine-tuning
NDCG	MRR	R@1	R@5	R@10	Mean	NDCG	MRR	R@1	R@5	R@10	Mean
MCA-I-H (L6 H8)	60.27	64.33	51.12	80.91	89.65	4.24	72.22	42.38	26.94	60.17	75.2	8.2
MCA-I-H (L2 H4)	58.99	64.46	51.14	81.03	89.91	4.19	70.57	42.48	26.3	61.3	76.05	8.06
MCA-I-H (L6 H2)	60.13	60.63	46.7	77.55	87.47	4.8	70.42	39.17	23.3	57.64	73.48	8.69
Table 7: Hyper-parameter tuning for number of layers and number of heads. The results in the main paper are reported with 6 Layers(L6) and 8 Heads (H8) for all MCA models.
B AMT Interface
Here, we provide more details on the crowdsourcing study described in Section 6.1. Figure 6 shows the instructions shown to the turkers. We also setup a qualification test consisting of 2 test images (in Figure 7) to assess whether turkers understood the task properly. This allowed us to have an automated quality check for the annotations. Each HIT consisted of 15 images. For the actual task (e.g. Fig. 8), users were shown just the image and the current question – without any previous historical context – and asked to choose one of the answers as shown in Table 6. Our AMT interface<sup>11</sup> used AWS boto3 library in python.
C Diversity and dialog phenomena in VisDial dataset
We also did an analysis of the top-20 questions (Figure 9) and answers (Figure 10) in the training set. 'Yes'/‘No' binary answers form the major chunk  $(19.15\%)$  and  $21.2\%$  respectively) of ground truth answers. Color related answers (such as White, Brown in the top-20 answers) form  $4 \%$  of all the ground truth answers. Numbered answers (such as 0, 1, 2 ,3) form  $1.3\%$  while Can't tell' form another  $1.2\%$ .
As evident in the top-20 questions, weather related questions (such as 'Is it sunny/daytime/day/night?'), color related ('What color is it/his hair/the table?') and basic conversational-starters ('Can you see any people?') form the major portion.
We also tried to analyze the top-20 answers (Figure 11) which had non-zero relevance in the dense annotations. Specifically, we took all  $2\mathrm{k}$  example turns of training set with dense annotations for each of 100 options. We find that generic answers such as 'Can't tell', binary answers 'Yes/No' and their semantically equivalent answers 'Not that i can see' are mostly given non-zero relevance by crowd-workers.
We tried to calculate the statistics of the pronouns and ellipsis which we consider essential (but not complete) phenomena in a dialog dataset. Figure 12 shows the number of pronouns in a dialog. We find that major chunk consisted of 2-6 pronouns in all the 10 questions across the dialog. We tried to distinguish between the usage of 'it' as pleonastic and non-pleonastic pronouns (discussed in (Loaiciga et al., 2017)). For e.g. in the sentence: 'It is raining'. Here, though, 'it' would be identified as a pronoun, but it doesn't refer to anything. Notice the drift in distribution of the number of pronouns (All pronouns vs Non-pleonastic). We also tried to identify the cases of ellipsis (methodology explained further) and found that majority questions  $(82\%)$  doesn't contain any case of ellipsis in the dialog. We define simple heuristics to identify dialog phenomena. Specifically, our heuristics can be listed as:
- We use constituency parser (Joshi et al., 2018) to parse each question. If the parsed tree doesn't contain 'Sentence' as the root ('S', 'SQ', 'SBARQ', 'SINV'), we consider it a case of ellipsis.
- We use spaCy  $^{13}$  to extract the pronouns in all the questions of a dialog.
- To distinguish between different usage of 'it', we mark all the co-occurrences of manually defined weather identifiers ('rainy', 'sunny', 'daytime', 'day', 'night') as pleonastic.
- Though 'other' is a pronoun, it is not tagged by standard taggers. We explicitly deal with these cases to tag 'other' as a case of pronoun. For e.g. 'What about the other?'
D Corrected dense annotations
We maintain the whole relevance list, however we change the relevance of only the ground truth (GT) to 1 instead of  $0 / 0.5$  in the train annotations (only 943 values). This was done to avoid extra gradient
12 https://github.com/allenai/allennlp/ blob/master/allennlp/pretrained.py
13 https://spacy.io/usage/ linguistic-features
<sup>11</sup>We built upon the repo: https://github.com/jcjohnson/simple-amt.
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MCA-I-H
MCA-I-VGH
A surfer crouches as they ride a cresting wave.  $N_{Rel}$  : 15
An apple and orange are sitting in a white box with size measurements.