In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them. We argue that such data can prove as a testing ground for understanding how we reason about information. To study this, we introduce a new dataset called INFOTABS, comprising of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. Our analysis shows that the semi-structured, multi-domain and heterogeneous nature of the premises admits complex, multi-faceted reasoning. Experiments reveal that, while human annotators agree on the relationships between a table-hypothesis pair, several standard modeling strategies are unsuccessful at the task, suggesting that reasoning about tables can pose a difficult modeling challenge. Recent progress in text understanding has been driven by sophisticated neural networks based on contextual embeddingse.g., BERT (Devlin et al., 2019), and its descendantstrained on massive datasets, such as SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018), and SQuAD (Ra-jpurkar et al., 2016). Several such models outperform human baselines on these tasks on the benchmark suites such as GLUE (Wang et al., 2019b). Reasoning about text requires a broad array of skillsmaking lexical inferences, interpreting the nuances of time and locations, and accounting for world knowledge and common sense. Have we achieved human-parity across such a diverse collection of reasoning skills? In this paper, we study this question by proposing an extension of the natural language inference (NLI) task (Dagan et al., 2005, and others). In Dressage Highestgoverning body International Federation for Equestrian Sports (FEI) Characteristics Contact No Team members Individual and team at international levels Mixed gender Yes Equipment Horse, horse tack Venue Arena, indoor or outdoor Presence Country or region Worldwide Olympic 1912 Paralympic 1996 H1: Dressage was introduced in the Olympic games in 1912. H2: Both men and women compete in the equestrian sport of Dressage. H3: A dressage athlete can participate in both individual and team events. H4: FEI governs dressage only in the U.S. Figure 1: A semi-structured premise (the table). NLI, which asks whether a premise entails, contradicts or is unrelated to a hypothesis, the premise and the hypothesis are one or more sentences. Understanding the premise requires understanding its linguistic structure and reasoning about it. We seek to separate these two components. Our work stems from the observation that we can make valid inferences about implicit information conveyed by the mere juxtaposition of snippets of text, as shown in the table describing Dressage in Figure 1. We introduce the INFOTABS dataset to study and model inference with such semi-structured data. Premises in our dataset consist of info-boxes that convey information implicitly, and thus require complex reasoning to ascertain the validity of hypotheses. For example, determining that the hypothesis H2 in Figure 1 entails the premise table requires looking at multiple rows of the table, understanding the meaning of the row labeled Mixed gender , and also that Dressage is a sport. INFOTABS consists of 23,738 premise-hypothesis pairs, where all premises are info-boxes, and the hypotheses are short sentences. As in the NLI task, the objective is to ascertain whether the premise entails, contradicts or is unrelated to the hypothesis. The dataset has 2,540 unique info-boxes drawn from Wikipedia articles across various categories, and all the hypotheses are written by Amazon's Mechanical Turk workers. Our analysis of the data shows that ascertaining the label typically requires the composing of multiple types of inferences across multiple rows from the tables in the context of world knowledge. Separate verification experiments on subsamples of the data also confirm the high quality of the dataset. We envision our dataset as a challenging testbed for studying how models can reason about semi-structured information. To control for the possibility of models memorizing superficial similarities in the data to achieve high performance, in addition to the standard train/dev/test split, our dataset includes two additional test sets that are constructed by systematically changing the surface forms of the hypothesis and the domains of the tables. We report the results of several families of approaches representing word overlap based models, models that exploit the structural aspect of the premise, and also derivatives of state-of-the-art NLI systems. Our experiments reveal that all these approaches underperform across the three test sets. In summary, our contributions are: 1. We propose a new English natural language inference dataset, INFOTABS, to study the problem of reasoning about semi-structured data. 2. To differentiate models' ability to reason about the premises from their memorization of spurious patterns, we created three challenge test sets with controlled differences that employ similar reasoning as the training set. 3. We show that several existing approaches for NLI underperform on our dataset, suggesting the need for new modeling strategies. The dataset, along with associated scripts, are available at https://infotabs.github.io/ . We often encounter textual information that is neither unstructured (i.e., raw text) nor strictly structured (e.g., databases). Such data, where a structured scaffolding is populated with free-form text, can range from the highly verbose (e.g., web pages) to the highly terse (e.g. fact sheets, information tables, technical specifications, material safety sheets). Unlike databases, such semi-structured data can be heterogeneous in nature, and not characterized by pre-defined schemas. Moreover, we may not always have accompanying explanatory text that provides context. Yet, we routinely make inferences about such heterogeneous, incomplete information and fill in gaps in the available information using our expectations about relationships between the elements in the data. Understanding semi-structured information requires a broad spectrum of reasoning capabilities. We need to understand information in an ad hoc layout constructed with elements (cells in a table) that are text snippets, form fields or are themselves substructured (e.g., with a list of elements). Querying such data can require various kinds of inferences. At the level of individual cells, these include simple lookup (e.g., knowing that dressage takes place in an arena ), to lexical inferences (e.g., understanding that Mixed Gender means both men and women compete), to understanding types of text in the cells (e.g., knowing that the number 1912 is a year). Moreover, we may also need to aggregate information across multiple rows (e.g., knowing that dressage is a non-contact sport that both men and women compete in ), or perform complex reasoning that combines temporal information with world knowledge. We argue that a true test of reasoning should evaluate the ability to handle such semi-structured information. To this end, we define a new task modeled along the lines of NLI, but with tabular premises and textual hypotheses, and introduce a new dataset INFOTABS for this task. Before describing the new dataset, we will characterize our approach for a successful evaluation of automated reasoning. Recent work has shown that many datasets for NLI contain annotation biases or artifacts (e.g. Poliak et al., 2018). In other words, large models trained on such datasets are prone to learning spurious patternsthey can predict correct labels even with incomplete or noisy inputs. For instance, not and no in a hypothesis are correlated with contradictions (Niven and Kao, 2019). Indeed, classi-fiers trained on the hypotheses only (ignoring the premises completely) report high accuracy; they exhibit hypothesis bias , and achieving a high predictive performance does not need models to discover relationships between the premise and the hypothesis. Other artifacts are also possible. For example, annotators who generate text may use systematic patterns that leak information about the label to a model. Or, perhaps models can learn correlations that mimic reasoning, but only for one domain. With millions of parameters, modern neural networks are prone to overfitting to such imperceptible patterns in the data. From this perspective, if we seek to measure a model's capability to understand and reason about inputs, we cannot rely on a single fixed test set to rank models. Instead, we need multiple test sets (of similar sizes) that have controlled differences from each other to understand how models handle changes along those dimensions. While all the test sets address the same task, they may not all be superficially similar to the training data. With this objective, we build three test sets, named  1 ,  2 and  3 . Here, we briefly introduce them;  4 goes into specifics. Our first test set (  1 ) has a similar distribution as the training data in terms of lexical makeup of the hypotheses and the premise domains. The second, adversarial test set (  2 ) , consists of examples that are also similar in distribution to the training set, but the hypothesis labels are changed by expert annotators changing as few words in the sentence as possible. For instance, if Album X was released in the 21 st century is an entailment, the sentence Album X was released before the 21 st century is a contradiction, with only one change. Models that merely learn superficial textual artifacts will get confused by the new sentences. For  2 , we rewrite entailments as contradictions and vice versa, while the neutrals are left unaltered. Our third test set is the cross-domain (  3 ) set, which uses premises from domains that are not in the training split, but generally, necessitate similar types of reasoning to arrive at the entailment decision. Models that overfit domain-specific artifacts will underperform on  3 . Note that, in this work, we describe and introduce three different test sets, but we expect that future work can identify additional dimensions along which models overfit their training data and construct the corresponding test sets. In this section, we will see the details of the construction of INFOTABS. We adapted the general workflow of previous crowd sourcing approaches for creating NLI tasks (e.g., Bowman et al., 2015) that use Amazon's Mechanical Turk. 1 Sources of Tables Our dataset is based on 2 , 540 unique info-boxes from Wikipedia articles across multiple categories (listed in Appendix D). We did not include tables that have fewer than 3 rows, or have non-English cells (e.g., Latin names of plants) and technical information that may require expertise to understand (e.g., astronomical details about exoplanets). We also removed non-textual information from the table, such as images. Finally, we simplified large tables into smaller ones by splitting them at sub-headings. Our tables are isomorphic to key-value pairs, e.g., in Figure 1, the bold entries are the keys, and the corresponding entries in the same row are their respective values. Sentence generation Annotators were presented with a tabular premise and instructed to write three self-contained grammatical sentences based on the tables: one of which is true given the table, one which is false, and one which may or may not be true. The turker instructions included illustrative examples using a table and also general principles to bear in mind, such as avoiding information that is not widely known, and avoiding using information that is not in the table (including names of people or places). The turkers were encouraged not to restate information in the table, or make trivial changes such as the addition of words like not or changing numerical values. We refer the reader to the project website for a snapshot of the interface used for turking, which includes the details of instructions. We restricted the turkers to be from English-speaking countries with at least a Master's quali-fication. We priced each HIT (consisting of one table) at 50  . Following the initial turking phase, we removed grammatically bad sentences and rewarded workers whose sentences involved multiple rows in the table with a 10% bonus. Appendix C gives additional statistics about the turkers. 1 Appendix A has more examples of tables with hypotheses. per table). 2 We partitioned these tables into training, development (Dev),  1 and  2 test sets. To prevent an outsize impact of influential turkers in a split, we ensured that the annotator distributions in the Dev and test splits are similar to that of the training split. We created the  2 test set from hypotheses similar to those in  1 , but from a separate set of tables, and perturbing them as described in  3. On an average,  2 . 2 words were changed per sentence to create  2 , with no more than 2 words changing in 72% of the hypotheses. The provenance of  2 ensures that the kinds of reasoning needed for  2 are similar to those in  1 and the development set. For the  3 test set, we annotated 200 additional tables belonging to domains not seen in the training set (e.g., diseases, festivals). As we will see in  5, hypotheses in these categories involve a set of similar types of reasonings as  1 , but with different distributions. In total, we collected 23 , 738 sentences split almost equally among entailments, contradictions, and neutrals. Table 1 shows the number of tables and premise-hypothesis pairs in each split. In all the splits, the average length of the hypotheses is similar. We refer the reader to Appendix D for additional statistics about the data. Validating Hypothesis Quality We validated the quality of the data using Mechanical Turk. For each premise-hypothesis in the development and the test sets, we asked turkers to predict whether the hypothesis is entailed or contradicted by, or is unrelated to the premise table. We priced this task at 36  for nine labels. 2 For tables with ungrammatical sentences, we repeated the HIT. As a result, a few tables in the final data release have more than 9 hypotheses. inter-annotator agreement scores with Cohen's Kappa scores (Artstein and Poesio, 2008) between 0 . 75 and 0 . 80 . In addition, we see a majority agreement (at least 3 out of 5 annotators agree) of range between 93 % and 97 % . Furthermore, the human accuracy agreement between the majority and gold label (i.e., the label intended by the writer of the hypothesis), for all splits is in range 80 % to 84 % , as expected given the difficulty of the task. To study the nature of reasoning that is involved in deciding the relationship between a table and a hypothesis, we adapted the set of reasoning categories from GLUE (Wang et al., 2019b) to table premises. For brevity, here we will describe the categories that are not in GLUE and defined in this work for table premises. Appendix B gives the full list with definitions and examples. Simple look up refers to cases where there is no reasoning and the hypothesis is formed by literally restating what is in the table as a sentence; multi-row reasoning requires multiple rows to make an inference; and subjective/out-of-table inferences involve value judgments about a proposition or reference to information out of the table that is neither well known or common sense. All definitions and their boundaries were verified via several rounds of discussions. Following this, three graduate students independently annotated 160 pairs from the Dev and  3 test sets each, and edge cases were adjudicated to arrive at consensus labels. Figures 2a and 2b summarizes these annotation efforts. We see that we have a multifaceted complex range of reasoning types across both sets. Importantly, we observe only a small number of simple lookups, simple negations for contradictions, and mere syntactic alternations that can be resolved without complex reasoning. Many instances call for looking up multiple rows, and involve temporal and numerical reasoning. Indeed, as Figures 2c and 2d show, a large number of examples need at least two distinct kinds of reasoning; on an average, sentences in the Dev and  3 sets needed 2.32 and 1.79 different kinds of reasoning, respectively. We observe that semi-structured premises forced annotators to call upon world knowledge and common sense (KCS); 48 . 75% instances in the Dev set require KCS. (In comparison, in the MultiNLI data, KCS is needed in 25 . 72% of examples.) We conjecture that this is because information about the entities and their types is not explicitly stated in tables, and have to be inferred. To do so, our annotators relied on their knowledge about the world including information about weather, seasons, and widely known social and cultural norms and facts. An example of such common sense is the hypothesis that  X was born in summer for a person whose date of birth is in May in New York. We expect that the INFOTABS data can serve as a basis for studying common sense reasoning alongside other recent work such as that of Talmor et al. (2019), Neutral hypotheses are more inclined to being subjective/out-of-table because almost anything subjective or not mentioned in the table is a neutral statement. Despite this, we found that in all evaluations in Appendix E (except those involving the adversarial  2 test set), our models found neutrals almost as hard as the other two labels, with only an  3% gap between the F-scores of the neutral label and the next best label. The distribution of train, dev,  1 and  2 are similar because the premises are taken from the same categories. However, tables for  3 are from different domains, hence not of the same distribution as the previous splits. This difference is also re-flected in Figures 2a and 2b, as we see a different distribution of reasonings for each test set. This is expected; for instance, we cannot expect temporal reasoning from tables in a domain that does not contain temporal quantities. The goal of our experiments is to study how well different modeling approaches address the INFOTABS data, and also to understand the impact of various artifacts on them. First, we will consider different approaches for representing tables in ways that are amenable to modern neural models. A key aspect of the INFOTABS task that does not apply to the standard NLI task concerns how premise tables are represented. As baselines for future work, let us consider several different approaches. 1. Premise as Paragraph (Para) : We convert the premise table into paragraphs using fixed template applied to each row. For a table titled t , a row with key k and value v is written as the sentence The k of t are v . For example, for the table in Figure 1, the row with key Equipment gets mapped to the sentence The equipment of Dressage are horse, horse tack. We have a small number of exceptions: e.g., if the key is born or died , we use the following template: t was k on v . The sentences from all the rows in the table are concatenated to form the premise paragraph. While this approach does not result in grammatical sentences, it fits the interface for standard sentence encoders. 2. Premise as Sentence (Sent): Since hypotheses are typically short, they may be derived from a small subset of rows. Based on this intuition, we use the word mover distance (Kus-ner et al., 2015) to select the closest and the three closest sentences to the hypothesis from the paragraph representation (denoted by WMD-1 and WMD-3, respectively). 3. Premise as Structure 1 (TabFact) : Following Chen et al. (2020), we represent tables by a sequence of key : value tokens. Rows are separated by a semi-colon and multiple values for the same key are separated by a comma. 4. Premise as Structure 2 (TabAttn) : To study an attention based approach, such as that of Parikh et al. (2016), we convert keys and values into a contextually enriched vectors by first converting them into sentences using the Para approach above, and applying a contextual encoder to each sentence. From the token embeddings, we obtain the embeddings corresponding of the keys and values by mean pooling over only those tokens. Based on the various representations of tables described above, we developed a collection of models for the table inference problem, all based on standard approaches for NLI. Due to space constraints, Reasoning Types N u m be r o f E x a m p l e s 0 20 40 60 80 C o r e f E lli p s i s E n t i t y T y p e KCSL e x i c a l R e a s o n i n g M u l t i r o w N a m e d E n t i t y N e g a t i o n N u m e r i c a l Q u a n t i f i c a t i o n S i m p l e L o o k u p S u b j e c t i v e / O OTS y n t a c t i c A l t e r n a t i o n T e m p o r a l Contradiction NeutralEntailment (a) Number of examples per reasoning type in the Dev set Reasoning Types N u m be r o f E x a m p l e s 0 20 40 60 80 C o r e f E lli p s i s E n t i t y T y p e KCSL e x i c a l R e a s o n i n g M u l t i r o w N a m e d E n t i t y N e g a t i o n N u m e r i c a l Q u a n t i f i c a t i o n S i m p l e L o o k u p S u b j e c t i v e / O OTS y n t a c t i c A l t e r n a t i o n T e m p o r a l Contradiction NeutralEntailment (d) Number of reasonings per example in the  3 set Figure 2: Distribution of the various kinds of reasoning in the Dev and  3 sets. we give a brief description of the models here and refer the interested reader to the code repository for implementation details. For experiments where premises are represented as sentences or paragraphs, we evaluated a feature-based baseline using unigrams and bigrams of tokens. For this model (referred to as SVM ), we used the LibLinear library (Fan et al., 2008). For these representations, we also evaluated a collection of BERT-class of models. Following the standard setup, we encoded the premise-hypothesis pair, and used the classification token to train a classifier, specifically a two-layer feedforward network that predicts the label. The hidden layer had half the size of the token embeddings. We compared RoBERTa L (Large), RoBERTa B (Base) and BERTB (Base) in our experiments. We used the above BERT strategy for the TabFact representations as well. For the TabAttn representations, we implemented the popular decomposable attention model (Parikh et al., 2016) using the premise key-value embeddings and hypothesis token embeddings with 512 dimensional attend and compare layers. We implemented all our models using the Py-Torch with the transformers library (Wolf et al., 2019). We trained our models using Adagrad with a learning rate of 10  4 , chosen by preliminary experiments, and using a dropout value of 0.2. All our results in the following sections are averages of models trained from three different random seeds. Does our dataset exhibit hypothesis bias? Before we consider the question of whether we can model premise-hypothesis relationships, let us first see if a model can learn to predict the entailment label without using the premise, thereby exhibiting an undesirable artifact. We consider three classes of models to study hypothesis bias in INFOTABS. Hypothesis Only (hypo-only): The simplest way to check for hypothesis bias is to train a classifier using only the hypotheses. Without a premise, a classifier should fail to correlate the hypothesis and the label. We represent the hypothesis in two ways a) using unigrams and bigrams for an SVM, and b) using a single-sentence BERT-class model. The results of the experiments are given in Table 3. Model Dev  1  2  3 Majority 33.33 33.33 33.33 33.33 SVM 59.00 60.61 45.89 45.89 BERTB 62.69 63.45 49.65 50.45 RoBERTa B 62.37 62.76 50.65 50.8 RoBERTa L 60.51 60.48 48.26 48.89 Table 3: Accuracy of hypothesis-only baselines on the INFOTABS Dev and test sets Dummy or Swapped Premise: Another approach to evaluate hypothesis bias is to provide an unrelated premise and train a full entailment model. We evaluated two cases, where every premise is changed to a (a) dummy statement ( to be or not to be ), or (b) a randomly swapped table that is represented as paragraph. In both cases, we trained a RoBERTa L classifier as described in  6.2. The results for these experiments are presented in Table 4. Premise Dev  1  2  3 dummy 60.02 59.78 48.91 46.37 swapped 62.94 65.11 52.55 50.21 Table 4: Accuracy with dummy/swapped premises Results and Analysis: Looking at the Dev and  1 columns of Tables 3 and 4, we see that these splits do have hypothesis bias. All the BERT-class models discover such artifacts equally well. However, we also observe that the performance on  2 and  3 data splits is worse since the artifacts in the training data do not occur in these splits. We see a performance gap of  12% as compared to Dev and  1 splits in all cases. While there is some hypothesis bias in these splits, it is much less pronounced. An important conclusion from these results is that the baseline for all future models trained on these splits should be the best premise-free performance. From the results here, these correspond to the swapped setting. How do trained NLI systems perform on our dataset? Given the high leaderboard accuracies of trained NLI systems, the question of whether these models can infer entailment labels using a linearization of the tables arises. To study this, we trained RoBERTa L models on the SNLI and MultiNLI datasets. The SNLI model achieves an accuracy of 92.56 % on SNLI test set. The MultiNLI model achieves an accuracy of 89.0 % on matched and 88.99 % on the mismatched MultiNLI test set. We evaluate these models on the WMD-1 and the Para representations of premises. Results and Analysis: In Table 5, all the results point to the fact that pre-trained NLI systems do not perform well when tested on INFOTABS. We observe that full premises slightly improve performance over the WMD-1 ones. This might be due to a) ineffectiveness of WMD to identify the correct premise sentence, and b) multi-row reasoning. Does training on the paragraph/sentence representation of a premise help? The next set of experiments compares BERT-class models and SVM trained using the paragraph (Para) and sentence (WMD-n) representations. The results for these experiments are presented in Table 6. Premise Dev  1  2  3 Train with SVM Para 59.11 59.17 46.44 41.28 Train with BERTB Para 63.00 63.54 52.57 48.17 Train with RoBERTa B Para 67.2 66.98 56.87 55.36 Train with RoBERTa L WMD-1 65.44 65.27 57.11 52.55 WMD-3 72.55 70.38 62.55 61.33 Para 75.55 74.88 65.55 64.94 Table 6: Accuracy of paragraph and sentence premise representation reported on SVM, BERTB , RoBERTa B and RoBERTa L Results and Analysis: We find that training with the INFOTABS training set improves model performance significantly over the previous baselines, except for the simple SVM model which relies on unigrams and bigrams. We see that RoBERTa L outperforms its base variant and BERTB by around  9% and  14% respectively. Similar to the earlier observation, providing full premise is better than selecting a subset of sentences. Importantly,  2 and  3 performance is worse than  1 , not only suggesting the difficulty of these data splits, but also showing that models overfit both lexical patterns (based on  2 ) or domain-specific patterns (based on  3 ). Does training on premise encoded as structure help? Rather than linearizing the tables as sentences, we can try to encode the structure of the tables. We consider two representative approaches for this, TabFact and TabAttn, each associated with a different model as described in  6.2. The results for these experiments are listed in Table 7. Premise Dev  1  2  3 Train with BERTB TabFact 63.67 64.04 53.59 49.05 Train with RoBERT B TabFact 68.06 66.7 56.87 55.26 Train with RoBERTa L TabAttn 63.63 62.94 49.37 49.04 TabFact 77.61 75.06 69.02 64.61 Table 7: Accuracy on structured premise representation reported on BERTB , RoBERTa B and RoBERTa L Results and Analysis: The idea of using this family of models was to leverage the structural aspects of our data. We find that the TabAttn model, however, does not improve the performance. We assume that this might be due to the bag of words style of representation that the classifier employs. We find, however, that providing premise structure information helps the TabFact model perform better than the RoBERTa L +Para model. As before model performance drops for  2 and  3 . How many types of reasoning does a trained system predict correctly? Using a RoBERTa L , which was trained on the paragraph (Para) representation, we analyzed the examples in Dev and  3 data splits that were annotated by experts for their types of reasoning (  5). Figure 3 shows the summary of this analysis. Results and Analysis: Figures 3a and 3b show the histogram of reasoning types among correctly predicted examples. Compared to Figures 2a and 2b, we see a decrease in correct predictions across all reasoning types for both Dev and  3 sets. In particular, in the Dev set, the model performs poorly for the knowledge & common sense, multi-row, coreference, and temporal reasoning categories. Discussion Our results show that: 1) INFOTABS contains a certain amount of artifacts which transformer-based models learn, but all models have a large gap to human performance; and 2) models accuracies drop on  2 and  3 , suggesting that all three results together should be used to characterize the model, and not any single one of them. All our models are significantly worse than the human performance ( 84 . 04% , 83 . 88% and 79 . 33% for  1 ,  2 and  3 respectively). With a difference of  14% between our best model and the human performance, these results indicate that INFOTABS is a challenging dataset. NLI Datasets Natural language inference/textual entailment is a well studied text understanding task, and has several datasets of various sizes. The annual PASCAL RTE challenges (Dagan et al., 2005, inter alia) were associated with several thousands of human-annotated entailment pairs. The SNLI dataset (Bowman et al., 2015) is the first large scale entailment dataset that uses image captions as premises, while the MultiNLI (Williams et al., 2018) uses premises from multiple domains. The QNLI and WNLI datasets provide a new perspective by converting the SQuAD question answering data (Rajpurkar et al., 2016) and Winograd Schema Challenge data (Levesque et al., 2012) respectively into inference tasks. More recently, SciTail (Khot et al., 2018) and Adversarial NLI (Nie et al., 2019) have focused on building adversarial datasets; the former uses information retrieval to select adversarial premises, while the latter uses iterative annotation cycles to confuse models. Reasoning Recently, challenging new datasets have emerged that emphasize complex reasoning. Bhagavatula et al. (2020) pose the task of determining the most plausible inferences based on observation (abductive reasoning). Across NLP, a lot of work has been published around different kinds of reasonings. To name a few, common sense (Talmor et al., 2019), temporal (Zhou et al., 2019), numerical (Naik et al., 2019; Wallace et al., 2019b) and Reasoning Types N u m be r o f C o rr e c t P r ed i c t i on 0 20 40 60 80 C o r e f E lli p s i s E n t i t y T y p e KCSL e x i c a l R e a s o n i n g M u l t i r o w N a m e d E n t i t y N e g a t i o n N u m e r i c a l Q u a n t i f i c a t i o n S i m p l e L o o k u p S u b j e c t i v e / O OTS y n t a c t i c A l t e r n a t i o n T e m p o r a l Contradiction NeutralEntailment multi-hop (Khashabi et al., 2018) reasoning have all garnered immense research interest. Tables and Semi-structured data Tasks based on semi-structured data in the form of tables, graphs and databases (with entries as text) contain complex reasoning (Dhingra et al., 2019; Chen et al., 2020). Previous work has touched upon semantic parsing and question answering (e.g., Pasupat and Liang, 2015; Khashabi et al., 2016, and references therein), which typically work with tables with many entries that resemble database records. Our work is most closely related to TabFact (Chen et al., 2020), which considers database-style tables as premises with human-annotated hypotheses to form an inference task. While there are similarities in the task formulation scheme, our work presents an orthogonal perspective: (i) The Wikipedia tables premises of TabFact are homogeneous, i.e., each column in a table has structural redundancy and all entries have the same type. One can look at multiple entries of a column to infer extra information, e.g., all entries of a column are about locations. On the contrary, the premises in our dataset are heterogeneous. (ii) TabFact only considers entailment and contradiction; we argue that inference is non-binary with a third unde-termined class (neutrals). (iii) Compared to our multi-faceted reasonings, the reasonings of the hypotheses in TabFact are limited and mostly numerical or comparatives. (iv) The  2 and  3 sets help us check for annotation and domain-specific artifacts. Artifacts Recently, pre-trained transformer-based models (Devlin et al., 2019; Radford et al., 2019; Liu et al., 2019, and others) have seemingly outperformed human performance on several NLI tasks (Wang et al., 2019b,a). However, it has been shown by Poliak et al. (2018); Niven and Kao (2019); Gururangan et al. (2018); Glockner et al. (2018); Naik et al. (2018); Wallace et al. (2019a) that these models exploit spurious patterns (artifacts) in the data to obtain good performance. It is imperative to produce datasets that allow for controlled study of artifacts. A popular strategy today is to use adversarial annotation (Zellers et al., 2018; Nie et al., 2019) and rewriting of the input (Chen et al., 2020). We argue that we can systematically construct test sets that can help study artifacts along specific dimensions. We presented a new high quality natural language inference dataset, INFOTABS, with heterogeneous semi-structured premises and natural language hypotheses. Our analysis showed that our data encompasses several different kinds of inferences. INFOTABS has multiple test sets that are designed to pose difficulties to models that only learn superficial correlations between inputs and the labels, rather than reasoning about the information. Via extensive experiments, we showed that derivatives of several popular classes of models find this new inference task challenging. We expect that the dataset can serve as a testbed for developing new kinds of models and representations that can handle semi-structured information as first class citizens. We thank members of the Utah NLP group for their valuable insights and suggestions at various stages of the project; and reviewers their helpful comments. We acknowledge the support of the support of NSF Grants No. 1822877 and 1801446, and a generous gift from Google.
result abstain objective result abstain abstain abstain abstain result abstain abstain abstain abstain abstain abstain abstain abstain result abstain abstain abstain abstain abstain result abstain method result result result objective method objective other abstain abstain abstain abstain method method abstain method abstain abstain method method abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain method method abstain abstain abstain abstain abstain abstain abstain abstain method abstain method abstain method abstain abstain method method abstain abstain abstain abstain method abstain abstain method abstain abstain abstain abstain abstain method abstain abstain abstain abstain method abstain abstain abstain method method abstain abstain abstain abstain abstain abstain abstain abstain method method abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain method abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain result abstain other other abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain result abstain abstain abstain abstain abstain abstain abstain abstain other abstain abstain abstain abstain result abstain result result abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain result abstain abstain abstain abstain abstain result result abstain abstain abstain abstain abstain result result abstain result abstain abstain abstain abstain abstain result abstain result abstain result other other other other other other other other other other other other method method other other abstain method method abstain other other other other method objective result abstain objective objective other other