INFOTABS : Inference on Tables as Semi-structured Data (2020)

Abstract: 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.

## introduction recent progress in text understanding has been driven by sophisticated neural networks based on contextual embeddings-e.g., bert @xcite , and its descendants-trained on massive datasets, such as snli @xcite , multinli @xcite , and squad @xcite . several such models outperform human baselines on these tasks on the benchmark suites such as glue @xcite . reasoning about text requires a broad array of skills-making 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 @xcite . in dressage highest governing 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). two hypotheses (h1, h2) are entailed by it, h3 is neither entailed nor contradictory, and h4 is a contradiction. 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, un-derstanding the meaning of the row labeled mixed gender, and also that dressage is a sport. infotabs consists of 23,738 premisehypothesis 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 semistructured 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: ## the case for reasoning about semi-structured data 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. ## the need for multi-faceted evaluation 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. @xcite . in other words, large models trained on such datasets are prone to learning spurious patterns-they can predict correct labels even with incomplete or noisy inputs. for instance, not and no in a hypothesis are correlated with contra-dictions @xcite . indeed, classifiers 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 α 1 , α 2 and α 3 . here, we briefly introduce them; §4 goes into specifics. our first test set (α 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 (α 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 α 2 , we rewrite entailments as contradictions and vice versa, while the neutrals are left unaltered. our third test set is the cross-domain (α 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 α 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 con-struct the corresponding test sets. ## the infotabs dataset 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., @xcite 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 englishspeaking countries with at least a master's qualification. 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. ## reasoning analysis 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 @xcite 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 α 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 α 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 @xcite , 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 α 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, α 1 and α 2 are similar because the premises are taken from the same categories. however, tables for α 3 are from different domains, hence not of the same distribution as the previous splits. this difference is also reflected 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. ## experiments and results the goal of our experiments is to study how well different modeling approaches address the in-fotabs 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. ## conclusion 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 semistructured information as first class citizens.