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A Details: Table Representation
1. Premise as Paragraph: (Chen et al., 2020b), (Gupta et al., 2020) employ universal templates to construct close to natural language sentences for isolated cells in a
Hyper Parameter	XLM-RoBERTa	IndicBERT	MuRIL-base-cased	mBERT-cased
Initial Learning Rate	[1e-4,1e-9]	5e-5	5e-5	5e-5
Batch Size	128	128	128	128
Weight Decay	0.01	0.01	0.01	0.01
Max Seq Length	128	128	128	128
Model Size	278M	33.7M	237M	177M
Warmup Steps	500	500	500	500
Table 5: Hyper Parameters used for Fine-Tuning the corresponding multilingual models.
row, and then, concatenate them to obtain a single paragraph representation. (Gupta et al., 2020) suggest constructing sentences of the form "The  $k$  of  $t$  is  $v$ " for a cell having key  $k$ , value  $v$  in a table with title  $t$ . E.g. in figure 1 for the row Born the premise sentence would be "The born of Joe Strummer is 21 August 1952 (1952-08-21) Ankara, Turkey"
However, (Neeraja et al., 2021) identify that such templates can often lead to ungrammatical sentences and propose the Better Paragraph Representation (BPR) approach. BPR utilises type specific templates based on the entity type of a key, and the overall category of the table itself resulting in grammatical sentences. (Neeraja et al., 2021) note a significant increase in performance while employing BPR over the universal template. We adopt BPR as one of our representation approaches. E.g. for same Born key in figure 1 the premise sentence with BPR representation would be "Joe Strummer was born on August 21, 1952 (1952-08-21) at Ankara, Turkey"
2. Premise as Structure: Unlike the natural language like Premise as Paragraph representations, here, we try to represent the row as structural text as proposed by (Chen et al., 2020b). Every isolated cell in a row is represented as "k : v" where k is the key, and v is the value of the cell. A row's structural representation is a semi-colon ";" separated sequence of the structural representations of all the isolated cells in that row. E.g. for the same Born key in figure 1 the premise sentence will be represented as "Born : August 21, 1952 (1952-08-21), Ankara, Turkey"
B Details: Multilingual Models
Indic Specific: This class of multilingual models are pre-trained entirely on Indic language data along with English. We use MuRIL Base (Khanuja et al., 2021a), and IndicBERT (kak) pre-trained multilingual models. MuRIL is a BERT (Devlin et al., 2019) based model trained with Masked Language Modeling (Taylor, 1953) and Translation Language Modeling (CONNEAU and Lample, 2019) objectives. It is trained on (a.) Common Crawl OSCAR corpus<sup>7</sup> and Wikipedia<sup>8</sup> monolingual data for 16 Indic languages along with the English language, (b.) PMIndia (Haddow and Kirefu, 2020b) along with other in-house parallel corpora, (c.) and the Dakshina Dataset (Roark et al., 2020) along with other parallel in-house transliterated corpora. IndicBERT is an ALBERT (Lan et al., 2019) based model trained on IndicCorp (kak).
Generic: This class of multilingual models are pre-trained on a wide array of languages from around the world. We use mBERT-cased (Devlin et al., 2019) and XLM-RoBERTa (con) pre-trained multilingual models.
C Human Evaluation Strategy
We requested our colleagues who are native speakers and are proficient in English to help us with this task while disclosing the intentions. We provide them with instructions adopted from the Direct Assessment (Graham et al., 2013) strategy for low resource machine translation in (Guzman et al., 2019). We sample 50 pairs of source, translation pairs and ask the annotators to provide a continuous score between 0 to 100. 0–10 range represents a translation that is completely incorrect and inaccurate. 70–90 range represents a translation that closely preserves the meaning of the source sentence while the 90–100 range represents a perfect translation.
7 Oscar Corpus 8 Tensorflow Datasets
D Model Hyper-Parameters
Table 5 reports the hyper-parameters used for fine-tuning the multilingual models on EI-INFOTABS. We use the Huggingface Transformers library to script these experiments. We were unable to successfully converge XLM-RoBERTa in multiple runs spanning a distinctive set of hyper-parameters. Figure 5 shows the loss plots for XLM-RoBERTa and mBERT when fine-tuned on EI-INFOTABS. It is distinctively visible that XLM-RoBERTa is unable to converge on EI-INFOTABS on a significant amount of steps unlike mBERT.
Figure 5: Train Loss for multiple runs of XLM-RoBERTa with distinct set of hyper-parameters compared with that of mBERT. Each run spans roughly 37,000 steps.
Fine-Tuning Settings. We follow the conventionally used pipeline for fine-tuning BERT for Sequence Classification (Jiang and de Marneffe, 2019). We concatenate the premise and the hypothesis strings using a [SEP] token in between them, preprocess this sequence with a [CLS] token, tokenize this sequence using the pre-trained tokenizer for the respective model, and provide the obtained sequence as input to the pre-trained model. We attach a three-way classification head with cross-entropy loss on top of the pooled output obtained from the previous step. With an initial learning rate of 5e-05 with AdamW optimizer (Loshchilov and Hutter, 2018), we fine-tune each model on 4 1080Ti GPUs with a batch size of 32 per GPU over 10 epochs.
E Performance on the  $\alpha_{2}$  and  $\alpha_{3}$  Adversarial Sets
Tables 6 and 7 report the results for the adversarial test sets  $\alpha_{2}$  and  $\alpha_{3}$  respectively using the BPR linearization method.
F Zero Shot Cross-Linguual Transfer
Tables 8 and 9 report the performance of MuRIL on Translate-Train-X and Bilingual-Train-X. We note that models trained on linguistically closer pairs of languages are able to admirably transfer their performance to each other. Notably, Assamese ('as') and Bengali ('bn') being immensely closely related, support this hypothesis. Moreover, we note the same for closely related Indo-European languages Bengali, Hindi, Gujarati ('gu'), and Marathi ('mr'). Models trained on these languages distinctively transfer their performance better on each other compared to languages from the Dravidian language family - Malayalam ('ml'), Telugu ('te'), Tamil ('ta'), Kannada ('kn'). Dravidian languages are not as closely related due to differences in scripts and sentence structures which is observed in the results as well.
9 Transformer Hugging Face
Strategy	Model	bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	ModAvg
Translate-Train	mBERT	51	52	48	49	48	48	49	48	49	47	36	48
IndicBERT	46	44	44	44	46	46	45	45	46	34	45	44
MuRIL	56*	56*	52	55	54	52	55	53	55	54	53	54
LnAvg	51	51	48	49	49	49	50	48	50	45	45	49
Translate-Test	mBERT	47	47	44	45	44	44	45	43	46	41	34	44
IndicBERT	41	38	37	38	39	38	38	38	38	42	39	39
MuRIL	52	51	51	50	50	51	51	53*	53*	49	50	51
LnAvg	47	45	44	44	44	45	44	44	45	44	41	44
Bilingual-Train	mBERT	52	52	50	51	50	50	51	49	51	50	37	49
IndicBERT	45	45	45	48	47	45	46	45	46	44	45	45
MuRIL	56*	55	54	56*	54	54	53	54	56*	54	53	54
LnAvg	51	51	50	51	50	50	50	49	51	49	45	50
Multilingual-Train	mBERT	50	51	51	50	50	51	49	48	50	48	35	48
IndicBERT	46	46	46	47	45	45	44	44	45	45	44	45
MuRIL	55	55	56*	55	55	54	55	55	55	54	54	55
LnAvg	50	51	51	51	50	50	49	49	50	49	45	50
EnTranslate-Test	mBERT	55	55	55	53	53	54	54	54	54	54	53	54
IndicBERT	48	47	47	48	47	47	46	46	46	47	47	47
MuRIL	56*	55	55	56*	54	55	54	55	54	54	54	55
LnAvg	53	52	52	52	51	52	51	52	51	52	52	52
Translate-Train-X	mBERT	45	45	44	45	42	42	45	41	45	40	35	43
IndicBERT	40	38	37	36	37	39	38	40	38	34	38	38
MuRIL	54*	53	52	54*	53	52	53	51	52	54*	52	53
LnAvg	46	45	44	45	44	44	45	44	45	42	41	44
Bilingual-Train-X	mBERT	47	47	46	46	46	46	46	44	46	47	43	46
IndicBERT	39	40	39	39	39	39	40	39	39	40	39	39
MuRIL	54	54	54	55*	54	53	53	54	54	54	54	54
LnAvg	46	47	46	47	47	46	46	46	46	47	45	46
Table 6: Performance in terms of accuracy when evaluated on the  $\alpha_{2}$  test set. Higher value implies better performance. Here, LnAvg represents the average accuracy for a language across all models, while ModAvg represents the average accuracy of a model across all languages. A value in Purple represents the best accuracy for that model across all languages. An underlined value in Blue represents the best accuracy for that language across all models. A value in Green with an asterisk(*) represents the cases where language-wise and model-wise values coincide. As we fine-tune on a specific Indic language in the fine-tuning strategies Translate-Train-X and Bilingual-Train-X, we report the training average of the concerned language. We do not include the results of XLM-RoBERTa as the model fails to converge on these experiments on multiple runs with a distinct set of hyper-parameters as explained in Appendix §D.