Title: RECLOR: A READING COMPREHENSION DATASET REQUIRING LOGICAL REASONING

Abstract: Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comprehension dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. As earlier studies suggest, human-annotated datasets usually contain biases, which are often exploited by models to achieve high accuracy without truly understanding the text. In order to comprehensively evaluate the logical reasoning ability of models on ReClor, we propose to identify biased data points and separate them into EASY set while the rest as HARD set. Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models.

1. INTRODUCTION:

Machine reading comprehension (MRC) is a fundamental task in Natural Language Processing, which requires models to understand a body of text and answer a particular question related to the context. With success of unsupervised representation learning in NLP, language pre-training based models such as GPT-2 (Radford et al., 2019) , BERT (Devlin et al., 2019) , XLNet (Yang et al., 2019) and RoBERTa (Liu et al., 2019) have achieved nearly saturated performance on most of the popular MRC datasets (Rajpurkar et al., 2016; Lai et al., 2017; Rajpurkar et al., 2018; Wang et al., 2018) . It is time to challenge state-of-the-art models with more difficult reading comprehension tasks and move a step forward to more comprehensive analysis and reasoning over text (Dua et al., 2019) .

In natural language understanding, logical reasoning is an important ability to examine, analyze and critically evaluate arguments as they occur in ordinary language according to the definition from Law School Admission Council (2019a). It is a significant component of human intelligence and is essential in negotiation, debate and writing etc. However, existing reading comprehension datasets have none or merely a small amount of data requiring logical reasoning, e.g., 0% in MCTest dataset (Richardson et al., 2013) and 1.2% in SQuAD (Rajpurkar et al., 2016) according to Sugawara & Aizawa (2016) . One related task is natural language inference, which requires models to label the logical relationships of sentence pairs. However, this task only considers three types of simple logical relationships and only needs reasoning at sentence-level. To push the development of models in logical reasoning from simple logical relationship classification to multiple complicated logical reasoning and from sentence-level to passage-level, it is necessary to introduce a reading comprehension dataset targeting logical reasoning.

A typical example of logical reasoning questions is shown in Table 1 . Similar to the format of multiple-choice reading comprehension datasets (Richardson et al., 2013; Lai et al., 2017) , it contains a context, a question and four options with only one right answer. To answer the question in this example, readers need to identify the logical connections between the lines to pinpoint the conflict, then understand each of the options and select an option that solves the conflict. Human minds need extensive training and practice to get used to complex reasoning, and it will take immense efforts for crowdsourcing workers to design such logical reasoning questions. Inspired by the datasets extracted from standardized examinations (Lai et al., 2017; Clark et al., 2018) , we build a dataset by selecting such logical reasoning questions from standardized exams such as GMAT2 and LSAT3 . We finally collect 6,138 pieces of logical reasoning questions, which constitute a Reading Comprehension dataset requiring logical reasoning (ReClor).

Human-annotated datasets usually contain biases (Schwartz et al., 2017; Cai et al., 2017; Bugert et al., 2017; Poliak et al., 2018; Gururangan et al., 2018; Zellers et al., 2019) , which are often exploited by neural network models as shortcut solutions to achieve high testing accuracy. For data points whose options can be selected correctly without knowing the contexts and questions, we classify them as biased ones. In order to fully assess the logical reasoning ability of the models, we propose to identify the biased data points and group them as EASY set, and put the rest into HARD set. Based on our experiments on these separate sets, we find that even the state-of-the-art models can only perform well on EASY set and struggle on HARD set as shown in Figure 1 . This phenomenon shows that current models can well capture the biases in the dataset but lack the ability to understand the text and reason based on connections between the lines. On the other hand, human beings perform similarly on both the EASY and HARD set. It is thus observed that there is still a long way to go to equip models with true logical reasoning ability.

The contributions of our paper are two-fold. First, we introduce ReClor, a new reading comprehension dataset requiring logical reasoning. We use option-only-input baselines trained with different random seeds to identify the data points with biases in the testing set, and group them as EASY set, with the rest as HARD set to facilitate comprehensive evaluation. Second, we evaluate several stateof-the-art models on ReClor and find these pre-trained language models can perform well on EASY set but struggle on the HARD set. This indicates although current models are good at exploiting biases in the dataset, they are far from capable of performing real logical reasoning yet. 

2. RELATED WORK:

Reading Comprehension Datasets. A variety of reading comprehension datasets have been introduced to promote the development of this field. MCTest (Richardson et al., 2013 ) is a dataset with 2,000 multiple-choice reading comprehension questions about fictional stories in the format similar to ReClor. Rajpurkar et al. (2016) proposed SQuAD dataset, which contains 107,785 questionanswer pairs on 536 Wikipedia articles. The authors manually labeled 192 examples of the dataset and found that the examples mainly require reasoning of lexical or syntactic variation. In an analysis of the above-mentioned datasets, Sugawara & Aizawa (2016) found that none of questions requiring logical reasoning in MCTest dataset (Richardson et al., 2013) and only 1.2% in SQuAD dataset (Rajpurkar et al., 2016) . Lai et al. (2017) introduced RACE dataset by collecting the English exams for middle and high school Chinese students in the age range between 12 to 18. They hired crowd workers on Amazon Mechanical Turk to label the reasoning type of 500 samples in the dataset and show that around 70 % of the samples are in the category of word matching, paraphrasing or single-sentence reasoning. To encourage progress on deeper comprehension of language,

Context::

In jurisdictions where use of headlights is optional when visibility is good, drivers who use headlights at all times are less likely to be involved in a collision than are drivers who use headlights only when visibility is poor. Yet Highway Safety Department records show that making use of headlights mandatory at all times does nothing to reduce the overall number of collisions.

Question: Which one of the following, if true, most helps to resolve the apparent discrepancy in the information above? Options:

A. In jurisdictions where use of headlights is optional when visibility is good, one driver in four uses headlights for daytime driving in good weather. B. Only very careful drivers use headlights when their use is not legally required. C. The jurisdictions where use of headlights is mandatory at all times are those where daytime visibility is frequently poor. D. A law making use of headlights mandatory at all times is not especially difficult to enforce. Answer: B Table 1 : An example in the ReClor dataset which is modified from the Law School Admission Council (2019b).

more reading comprehension datasets requiring more complicated reasoning types are introduced, such as iterative reasoning about the narrative of a story (Kočiskỳ et al., 2018) , multi-hop reasoning across multiple sentences (Khashabi et al., 2018) and multiple documents (Welbl et al., 2018) , commonsense knowledge reasoning (Mihaylov et al., 2018; Zhang et al., 2018; Huang et al., 2019) and numerical discrete reasoning over paragraphs (Dua et al., 2019) . However, to the best of our knowledge, although there are some datasets targeting logical reasoning in other NLP tasks mentioned in the next section, there is no dataset targeting evaluating logical reasoning in reading comprehension task. This work introduces a new dataset to fill this gap.

Logical Reasoning in NLP. There are several tasks and datasets introduced to investigate logical reasoning in NLP. The task of natural language inference, also known as recognizing textual entailment (Fyodorov et al., 2000; Condoravdi et al., 2003; Bos & Markert, 2005; Dagan et al., 2005; MacCartney & Manning, 2009) requires models to take a pair of sentence as input and classify their relationship types, i.e., ENTAILMENT, NEUTRAL, or CONTRADICTION. SNLI (Bowman et al., 2015) and MultiNLI (Williams et al., 2018) datasets are proposed for this task. However, this task only focuses on sentence-level logical relationship reasoning and the relationships are limited to only a few types. Another task related to logical reasoning in NLP is argument reasoning comprehension task introduced by Habernal et al. (2018) with a dataset of this task. Given an argument with a claim and a premise, this task aims to select the correct implicit warrant from two options. Although the task is on passage-level logical reasoning, it is limited to only one logical reasoning type, i.e., identifying warrants. ReClor and the proposed task integrate various logical reasoning types into reading comprehension, with the aim to promote the development of models in logical reasoning not only from sentence-level to passage-level, but also from simple logical reasoning types to the complicated diverse ones.

Datasets from Examinations. There have been several datasets extracted from human standardized examinations in NLP, such as RACE dataset (Lai et al., 2017) mentioned above. Besides, NTCIR QA Lab (Shibuki et al., 2014) offers comparative evaluation for solving real-world university entrance exam questions; The dataset of CLEF QA Entrance Exams Task (Rodrigo et al., 2015) is extracted from standardized English examinations for university admission in Japan; ARC dataset (Clark et al., 2018) consists of 7,787 science questions targeting student grade level, ranging from 3rd grade to 9th; The dialogue-based multiple-choice reading comprehension dataset DREAM (Sun et al., 2019) contains 10,197 questions for 6,444 multi-turn multi-party dialogues from English language exams that are designed by human experts to assess the comprehension level of Chinese learners of English.

Compared with these datasets, ReClor distinguishes itself by targeting logical reasoning.

3. RECLOR DATA COLLECTION AND ANALYSIS:

3.1 DATA COLLECTION

The format of data in ReClor is similar to other multiple-choice reading comprehension datasets (Richardson et al., 2013; Lai et al., 2017) , where a data point contains a context, a question and four answer options, among which only one option is right/most suitable. We collect reading comprehension problems that require complicated logical reasoning. However, producing such data requires the ability to perform complex logical reasoning, which makes it hard for crowdsourcing workers to generate such logical questions. Fortunately, we find the reading comprehension problems in some standardized tests, such as GMAT and LSAT, are highly in line with our expectation.

We construct a dataset containing 6,138 logical reasoning questions sourced from open websites and books. In the original problems, there are five answer options in which only one is right. To comply with fair use of law4 , we shuffle the order of answer options and randomly delete one of the wrong options for each data point, which results in four options with one right option and three wrong options. Furthermore, similar to ImageNet dataset5 , ReClor is available for non-commercial research purpose only. We are also hosting a public evaluation server on EvalAI (Yadav et al., 2019) to benchmark progress on Reclor.

3.2. DATA ANALYSIS:

As mentioned above, we collect 6,138 data points, in which 91.22% are from actual exams of GMAT and LSAT while others are from high-quality practice exams. They are divided into training set, validation set and testing set with 4,638, 500 and 1,000 data points respectively. The overall statistics of ReClor and comparison with other similar multiple-choice MRC datasets are summarized in Table 2. As shown, ReClor is of comparable size and relatively large vocabulary size. Compared with RACE, the length of the context of ReCor is much shorter. In RACE, there are many redundant sentences in context to answer a question. However, in ReClor, every sentence in the context passages is important, which makes this dataset focus on evaluating the logical reasoning ability of models rather than the ability to extract relevant information from a long context. The length of answer options of ReClor is largest among these datasets. We analyze and manually annotate the types of questions on the testing set and group them into 17 categories, whose percentages and descriptions are shown in Table 3 . The percentages of different types of questions reflect those in the logical reasoning module of GMAT and LSAT. Some examples of different types of logical reasoning are listed in Figure 2 , and more examples are listed in the Appendix C. Taking two examples, we further express how humans would solve such questions in Table 4 , showing the challenge of ReClor.

3.3. DATA BIASES IN THE DATASET:

The dataset is collected from exams devised by experts in logical reasoning, which means it is annotated by humans and may introduce biases in the dataset. Recent studies have shown that models can utilize the biases in a dataset of natural language understanding to perform well on the task without truly understanding the text (Schwartz et al., 2017; Cai et al., 2017; Bugert et al., 2017; Poliak et al., 2018; Gururangan et al., 2018; Zellers et al., 2019) . It is necessary to analyze such data biases to help evaluate models. In the ReClor dataset, the common context and question are shared across the four options for each data point, so we focus on the analysis of the difference in lexical choice and sentence length of the right and wrong options without contexts and questions.

We first investigate the biases of lexical choice. We lowercase the options and then use WordPiece tokenization (Wu et al., 2016) of BERT BASE (Devlin et al., 2019) to get the tokens. Similar to 2018), for the tokens in options, we analyze their conditional probability of label l ∈ {right, wrong} given by the token t by p(l|t) = count(t, l)/count(t). The larger the correlation score is for a particular token, the more likely it contributes to the prediction of related option. 

4. EXPERIMENTS:



4.1. BASELINE MODELS:

Many neural network based models such as FastText (Joulin et al., 2017) , Bi-LSTM, GPT (Radford et al., 2018) , GPT-2 (Radford et al., 2019) , BERT (Devlin et al., 2019) , XLNet (Yang et al., 2019) ,

If the purpose of laws is to contribute to people's happiness, we have a basis for criticizing existing laws as well as proposing new laws. Hence, if that is not the purpose, then we have no basis for the evaluation of existing laws, from which we must conclude that existing laws acquire legitimacy simply because they are the laws Question: The reasoning in the argument is flawed in that the argument Options:

A. takes a sufficient condition for a state of affairs to be a necessary condition for it B. draws a conclusion about how the world actually is on the basis of claims about how it should be C. infers a causal relationship from the mere presence of a correlation D. trades on the use of a term in one sense in a premise and in a different sense in the conclusion Answer: A Reasoning Process of Humans:

We may first look at the question to understand the specific task of the question -identify a flaw. We then analyze the argument in the context. The conclusion 'existing laws acquire legitimacy simply because they are the laws.' is based on the argument (purpose is NOT happiness) → (NOT basis for criticizing laws), which is obtained from the first statement: (purpose is happiness) → (basis for criticizing laws). However, we know ¬A → ¬B cannot be obtained from A → B. Therefore, we should choose option A that describes this flaw.

The distractors here are different types of reasoning flaws. Prior knowledge of basic logical rules is needed to correctly answer this question.

Psychologist: Phonemic awareness, or the knowledge that spoken language can be broken into component sounds, is essential for learning to read an alphabetic language. But one also needs to learn how sounds are symbolically represented by means of letters; otherwise, phonemic awareness will not translate into the ability to read an alphabetic language. Yet many children who are taught by the whole-language method, which emphasizes the ways words sound, learn to read alphabetic languages.

Question: Which one of the following can be properly inferred from the psychologist's statements? Options:

A. The whole-language method invariably succeeds in teaching awareness of how spoken language can be broken into component sounds. B. Some children who are taught by the whole-language method are not prevented from learning how sounds are represented by means of letters. C. The whole-language method succeeds in teaching many children how to represent sounds symbolically by means of letters. D. When the whole-language method succeeds in teaching someone how to represent sounds by means of letters, that person acquires the ability to read an alphabetic language.

Answer: B Reasoning Process of Humans:

Looking at the question and we know that it is asking about implication. From the first two sentences in context, we know that there are two necessary conditions to read an alphabetic language: phonemic awareness and symbolic letters. We also learn [(NOT symbolic letters) AND (phonemic awareness)] → read an alphabetic language (denoted as Formula 1). The last sentence in the context says that many children are taught by the whole-language method to learn a language. As for option A, from the context, we only know the whole language method works for 'many' children, which cannot be inferred to 'invariably' works. As for option B, combing three sentences in the context, we know that the whole-language method meets the two necessary conditions to learn a language, especially the last sentence mentions 'learn to read alphabetic languages'. Children learn to read alphabetic languages means that they must recognize symbolic letters that represent sound because symbolic letters is a necessary condition of read an alphabetic language; otherwise, they cannot read because of Formula 1 mentioned above. Therefore, option B is correct. As for option C, from the context we only know the whole-language method teaches phonemic awareness and read an alphabetic language. Symbolic letters may be taught by other methods, so C is wrong. As for D, similar to C, symbolic letters may be taught by other methods and we also cannot obtain: symbolic letters → read an alphabetic language.

Table 4 : Two examples to show how humans would solve the questions.

Necessary Assumptions:



The current pattern of human consumption of resources, in which we rely on nonrenewable resources, for example metal ore, must eventually change. Since there is only so much metal ore available, ultimately, we must either do without or turn to renewable resources to take its place.

Q: Which one of the following is an assumption required by the argument?:

A. We cannot indefinitely replace exhausted nonrenewable resources with other nonrenewable resources. B. Consumption of nonrenewable resources will not continue to increase in the near future. C. There are renewable resource replacements for all of the nonrenewable resources currently being consumed. D. Ultimately we cannot do without nonrenewable resources.

Context: Some theorists argue that literary critics should strive to be value-neutral in their literary criticism. These theorists maintain that by exposing the meaning of literary works without evaluating them, critics will enable readers to make their own judgments about the works' merits. But literary criticism cannot be completely value-neutral. Thus, some theorists are mistaken about what is an appropriate goal for literary criticism.

Q: The argument's conclusion follows logically if which one of the following is assumed?

A. Any critic who is able to help readers make their own judgments about literary works' merits should strive to produce value-neutral criticism. B. If it is impossible to produce completely value-neutral literary criticism, then critics should not even try to be value-neutral. C. The less readers understand the meaning of a literary work, the less capable they will be of evaluating that work's merits.. D. Critics are more likely to provide criticisms of the works they like than to provide criticisms of the works they dislike.

The television show Henry was not widely watched until it was scheduled for Tuesday evenings immediately after That' s Life, the most popular show on television. During the year after the move, Henry was consistently one of the ten most-watched shows on television. Since Henry' s recent move to Wednesday evenings, however, it has been watched by far fewer people. We must conclude that Henry was widely watched before the move to Wednesday evenings because it followed That' s Life and not because people especially liked it.

Q: Which one of the following, if true, most strengthens the argument? A. The show that now follows That's Life on Tuesdays has double the number of viewers it had before being moved. B. Henry has been on the air for three years, but That's Life has been on the air for only two years. C. After its recent move to Wednesday, Henry was aired at the same time as the second most popular show on television. D. That's Life was not widely watched during the first year it was aired.

Context: When a certain gland becomes cancerous in humans, it produces high levels of a particular protein. A blood test can determine the level of this protein well before a cancer of the gland could be detected by other means. Some doctors recommend that aggressive anticancer treatment should be begun as early as possible for anyone who is tested and is found to have high levels of the protein.

Q: Which one of the following, if true, most seriously weakens the doctors' recommendation?

A. The blood test for the protein has been in use for some time to monitor the condition of patients who have been diagnosed as having cancer of the gland. B. Before the blood test became available, about one third of all cases of cancer of the gland were detected in early stages. C. So far, no patients whose protein levels were found to be normal have subsequently developed cancer of the gland. D. Enlargement of the gland, a common condition infrequently associated with cancer, results in high levels of the protein.

Context: In a study, pairs of trained dogs were placed side by side and given a command such as "sit". After both obeyed the command, one dog was given a treat while its partner was given no reward at all. Over time, the dogs who went unrewarded began to disobey the command. This shows that dogs have an aversion to being treated unfairly.

Q: Which one of the following would be most useful to know in order to evaluate the argument? A. Were dogs who were accustomed to receiving regular rewards prior to the study more inclined to obey the command? B. How many repetitions were required before the unrewarded dogs began to disobey the command? C. Is there a decline in obedience if rewards are withheld from both dogs in the pair? D. Were dogs who received treats in one trial ever used as dogs that did not receive treats in other trials? RoBERTa (Liu et al., 2019) have achieved impressive results in various NLP tasks. We challenge these neural models with ReClor to investigate how well they can perform. Details of the baseline models and implementation are shown in the Appendix A and B.

4.2. EXPERIMENTS TO FIND BIASED DATA:

As mentioned earlier, biases prevalently exist in human-annotated datasets (Poliak et al., 2018; Gururangan et al., 2018; Zellers et al., 2019; Niven & Kao, 2019) , which are often exploited by models to perform well without truly understanding the text. Therefore, it is necessary to find out the biased data points in ReClor in order to evaluate models in a more comprehensive manner (Sugawara et al., 2018) . To this end, we feed the five strong baseline models (GPT, GPT-2, BERT BASE , XLNet BASE and RoBERTa BASE ) with ONLY THE ANSWER OPTIONS for each problem. In other words, we purposely remove the context and question in the inputs. In this way, we are able to identify those problems that can be answered correctly by merely exploiting the biases in answer options without knowing the relevant context and question. However, the setting of this task is a multiple-choice question with 4 probable options, and even a chance baseline could have 25% probability to get it right. To eliminate the effect of random guess, we set four different random seeds for each model and pick the data points that are predicted correctly in all four cases to form the EASY set. Then, the data points which are predicted correctly by the models at random could be nearly eliminated, since any data point only has a probability of (25%) 4 = 0.39% to be guessed right consecutively for four times. Then we unite the sets of data points that are consistently predicted right by each model, Published as a conference paper at ICLR 2020 because intuitively different models may learn different biases of the dataset. The above process is formulated as the following expression,

EQUATION

where C seed1 BERT denotes the set of data points which are predicted correctly by BERT BASE with seed 1, and similarly for the rest. Table 6 shows the average performance for each model trained with four different random seeds and the number of data points predicted correctly by all of them. Finally, we get 440 data points from the testing set C TEST and we denote this subset as EASY set C EASY and the other as HARD set C HARD . Table 6 : Average accuracy of each model using four different random seeds with only answer options as input, and the number of their common correct predictions.

Model:



4.3. TRANSFER LEARNING THROUGH FINE-TUNING:

Among multiple-choice reading comprehension or QA datasets from exams, although the size of ReClor is comparable to those of ARC (Clark et al., 2018) and DREAM (Sun et al., 2019) , it is much smaller than RACE Lai et al. (2017) . Recent studies (Min et al., 2017; Howard & Ruder, 2018; Huang et al., 2019; Jin et al., 2019) have shown the effectiveness of pre-training on similar tasks or datasets then fine-tuning on the target dataset for transfer learning. Jin et al. (2019) find that by first training on RACE (Lai et al., 2017) and then further fine-tuning on the target dataset, the performances of BERT BASE on multiple-choice dataset MC500 (Richardson et al., 2013) and DREAM (Sun et al., 2019) can significantly boost from 69.5% to 81.2%, and from 63.2% to 70.2%, respectively. However, they also find that the model cannot obtain significant improvement even performs worse if it is first fine-tuned on span-based dataset like SQuAD (Rajpurkar et al., 2016) .

ReClor is a multiple-choice dataset, so we choose RACE for fine-tuning study.

4.4. RESULTS AND ANALYSIS:

The performance of all tested models on the ReClor is presented in Table 7 . This dataset is built on questions designed for students who apply for admission to graduate schools, thus we randomly choose 100 samples from the testing set and divide them into ten tests, which are distributed to ten different graduate students in a university. We take the average of their scores and present it as the baseline of graduate students. The data of ReClor are carefully chosen and modified from only high-quality questions from standardized graduate entrance exams. We set the ceiling performance to 100% since ambiguous questions are not included in the dataset.

The performance of fastText is better than random guess, showing that word correlation could be used to help improve performance to some extent. It is difficult for Bi-LSTM to converge on this dataset. Transformer-based pre-training models have relatively good performance, close to the performance of graduate students. However, we find that these models only perform well on EASY set with around 75% accuracy, showing these models have an outstanding ability to capture the biases of the dataset, but they perform poorly on HARD set with only around 30% accuracy. In contrast, humans can still keep good performance on HARD set. We notice the difference in testing accuracy performed by graduate students on EASY and HARD set, but this could be due to the small number of students participated in the experiments. Therefore, we say humans perform relatively consistent on both biased and non-biased dataset.

It is noticed that if the models are first trained on RACE and then fine-tuned on ReClor, they could obtain significant improvement, especially on HARD set. The overall performance of RoBERTa LARGE is even better than that of graduate students. This similar phenomenon can also be observed on DREAM dataset (Sun et al., 2019) by Jin et al. (2019) , which shows the potential of transfer learning for reasoning tasks. However, even after fine-tuning on RACE, the best performance of these strong baselines on HARD set is around 50%, still lower than that of graduate students and far away from ceiling performance.

Experiments in different input settings are also done. Compared with the input setting of answer options only (A), the setting of questions and answer options (Q, A) can not bring significant improvement. This may be because some questions e.g., Which one of the following is an assumption required by the argument?, Which one of the following, if true, most strengthens the argument? can be used in the same reasoning types of question, which could not offer much information. Further adding context causes significant boost, showing the high informativeness of the context.

We further analyze the model performance with respect to different question types of logical reasoning. Some results are shown in Figure 4 and the full results are shown in Figure 5 

5. CONCLUSION:

In this paper, we introduce ReClor, a reading comprehension dataset requiring logical reasoning, with the aim to push research progress on logical reasoning in NLP forward from sentence-level to passage-level and from simple logical reasoning to multiple complicated one. We propose to identify biased data points and split the testing set into EASY and HARD group for biased and non-biased data separately. We further empirically study the different behaviors of state-of-the-art models on these two testing sets, and find recent powerful transformer-based pre-trained language models have an excellent ability to exploit the biases in the dataset but have difficulty in understanding and reasoning given the non-biased data with low performance close to or slightly better than random guess. These results show there is a long way to equip deep learning models with real logical reasoning abilities. We hope this work would inspire more research in future to adopt similar split technique and evaluation scheme when reporting their model performance. We also show by first fine-tuning on a large-scale dataset RACE then fine-tuning on ReClor, the models could obtain significant improvement, showing the potential of transfer learning to solve reasoning tasks. (0.9, 0.999) (0.9, 0.999) (0.9, 0.999) (0.9, 0.999) (0.9, 0.999) (0.9, 0.999) (0.9, 0.98) (0.9, 0.98) Clip Grad Norm Not Table 9 : Hyperparameters for finetuning pre-training language models on ReClor

C EXAMPLES:

Type: Necessary Assumptions Definition: identify the claim that must be true or is required in order for the argument to work Context: Slash-and-burn agriculture involves burning several acres of forest, leaving vegetable ash that provides ample fertilizer for three or four years of bountiful crops. On the cleared land nutrients leach out of the soil, however, and the land becomes too poor to support agriculture. New land is then cleared by burning and the process starts again. Since most farming in the tropics uses this method, forests in this region will eventually be permanently eradicated.

Question: The argument depends on the assumption that Options:

A. forests in the tropics do not regenerate well enough to restore themselves once they have been cleared by the slash-and-burn method B. some other methods of agriculture are not as destructive to the environment in tropical regions as the slash-and-burn method is C. forests in the tropics are naturally deficient in nutrients that are needed to support the growth of plants that are not native to those regions D. slash-and-burn agriculture is particularly suitable for farming in tropical areas Answer: A Table 10 : The definition and an example of the logical reasoning type -Necessary Assumptions

Type: Sufficient Assumptions Definition: identify a sufficient assumption, that is, an assumption that, if added to the argument, would make it logically valid Context: Geologist: A new method for forecasting earthquakes has reliably predicted several earthquakes. Unfortunately, this method can predict only that an earthquake will fall somewhere within a range of two and a half points on the Richter scale. Thus, since a difference of two and a half points can be the difference between a marginally perceptible shaking and a quake that causes considerable damage, the new method is unlikely to be useful. Question: Which one of the following, if assumed, enables the geologist's conclusion to be properly inferred? Options:

A. An earthquake-forecasting method is unlikely to be useful unless its predictions always differentiate earthquakes that are barely noticeable from ones that result in substantial destruction. B. Several well-established methods for forecasting earthquakes can predict within much narrower ranges than two and a half points on the Richter scale. C. Even if an earthquake-forecasting method makes predictions within a very narrow range on the Richter scale, this method is not likely to be useful unless its predictions are reliable.

D. An earthquake-forecasting method has not been shown to be useful until it has been used to reliably predict a large number of earthquakes. Answer: A Table 11 : The definition and an example of the logical reasoning type -Sufficient Assumptions

:

https://en.wikipedia.org/wiki/Graduate Management Admission Test

https://en.wikipedia.org/wiki/Law School Admission Test

https://www.copyright.gov/fair-use/more-info.html

http://image-net.org/download-faq

https://github.com/facebookresearch/fastText

https://github.com/huggingface/transformers

Figures and Tables:

Figure 1: Performance comparison of state-of-the-art models and humans (graduate students) on EASY and HARD set of ReClor testing set.

Figure 3: The distribution of the option length in ReClor with respect to right and wrong labels.

Figure 2: Examples of some question types. The correct options are marked by . More examples are shown in the Appendix C.

Figure 4: Performance of models on EASY (left) and HARD (right) testing sets and that of models. XLNet LARGE +Fine-Tune means the model is first fine-tuned on RACE before training on ReClor.

Statistics of several multiple-choice MRC datasets.

Table content: <table><tr><td/><td colspan="2">ReClor DREAM</td><td>MCTest</td><td>ARC</td><td>RACE</td></tr><tr><td>construction method</td><td>exams</td><td colspan="3">exams crowd-sourcing exams</td><td>exams</td></tr><tr><td>context type</td><td colspan="2">written text dialogues</td><td>child's stories</td><td colspan="2">-written text</td></tr><tr><td># of options</td><td>4</td><td>3</td><td>4</td><td>4</td><td>4</td></tr><tr><td># of context</td><td>6,138</td><td>6,444</td><td>660</td><td>-</td><td>27,933</td></tr><tr><td># of questions</td><td>6,138</td><td>10,197</td><td>2,640</td><td>7,787</td><td>97,687</td></tr><tr><td>Vocab size</td><td>26,576</td><td>13,037</td><td>8,000</td><td>6,329</td><td>136,629</td></tr><tr><td>Context Len</td><td>73.6</td><td>85.9</td><td>210.1</td><td>-</td><td>321.9</td></tr><tr><td>Question Len</td><td>17.0</td><td>8.6</td><td>7.8</td><td>20.5</td><td>10.0</td></tr><tr><td>Option Len</td><td>20.6</td><td>5.3</td><td>3.4</td><td>4.2</td><td>5.3</td></tr></table>

The percentage and description of each logical reasoning type. The descriptions are adapted from those specified by KhanAcademy (2019).

Table content: <table><tr><td>Poliak et al. (</td></tr></table>



Table content: <table><tr><td>Token motive ##ce thereby consequence warm interfere contributes</td><td colspan="2">Score (%) Freq 65.2 23 62.5 24 56.0 25 52.4 21 52.4 21 52.2 52.2 23 23</td><td>Probability</td><td>0.01 0.02 0.03 0.04 0.05</td><td/><td/><td/><td/><td/><td colspan="2">right wrong</td></tr><tr><td>manufacture included preferences</td><td>52.0 52.0 52.0</td><td>25 25 25</td><td/><td>0.00</td><td>0</td><td>10</td><td>20</td><td>30 Length</td><td>40</td><td>50</td><td>60</td></tr><tr><td colspan="3">Table 5: Top 10 tokens that correlate to right op-</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td colspan="2">tions with more than 20 occurrences.</td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr></table>

Accuracy (%) of models and human performance. The column Input means whether to input context (C), question (Q) and answer options (A). The RACE column represents whether to first use RACE to fine-tune before training on ReClor.

Table content: <table/>

, 6 and 7 in the Appendix E. Three models of BERT LARGE , XLNet LARGE and RoBERTa LARGE perform well on most of types. On HARD set, the three models perform poorly on certain types such as STRENGTHEN, WEAKEN and ROLE which require extensive logical reasoning. However, they perform relatively better on other certain types, such as CONCLUSION/MAIN POINT and MATCH STRUCTURES that are more straight-forward. For the result of transfer learning, we analyze XLNet LARGE in detail. Though the overall performance is significantly boosted after fine-tuning on RACE first, the histograms in the bottom of Figure4show that on EASY set, accuracy of the model with fine-tuning on RACE is similar to that without it among most question types, while on HARD set, significant improvement on some question types is observed, such as CONCLUSION/MAIN POINT and MOST STRONGLY SUPPORTED. This may be because these types require less logical reasoning to some extent compared with other types, and similar question types may also be found in RACE dataset. Thus, the pre-training on RACE helps enhance the ability of logical reasoning especially of relatively simple reasoning types, but more methods are still needed to further enhance the ability especially that of relatively complex reasoning types.

Table content: <table><tr><td colspan="2">100</td><td/><td/><td/><td colspan="3">BERTLARGE</td><td/><td/><td colspan="4">XLNetLARGE</td><td/><td colspan="4">RoBERTaLARGE</td><td colspan="2">100</td><td/><td/><td/><td colspan="3">BERTLARGE</td><td/><td/><td colspan="4">XLNetLARGE</td><td/><td colspan="3">RoBERTaLARGE</td></tr><tr><td>Accuracy (%)</td><td>40 60 80</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>Accuracy (%)</td><td>40 60 80</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td>20</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>20</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td>0</td><td>Necessary Assumptions</td><td>Sufficient Assumptions</td><td>Strengthen</td><td>Weaken</td><td>Evaluation</td><td>Implication</td><td>Conclusion/Main Point</td><td>Most Strongly Supported</td><td>Explain or Resolve</td><td>Principle</td><td>Dispute</td><td>Technique</td><td>Role</td><td>Identify a Flaw</td><td>Match Flaws</td><td>Match Structures</td><td>Others</td><td/><td>0</td><td>Necessary Assumptions</td><td>Sufficient Assumptions</td><td>Strengthen</td><td>Weaken</td><td>Evaluation</td><td>Implication</td><td>Conclusion/Main Point</td><td>Most Strongly Supported</td><td>Explain or Resolve</td><td>Principle</td><td>Dispute</td><td>Technique</td><td>Role</td><td>Identify a Flaw</td><td>Match Flaws</td><td>Match Structures</td><td>Others</td></tr><tr><td colspan="2">100</td><td/><td/><td/><td/><td/><td/><td colspan="4">XLNetLARGE</td><td/><td colspan="6">XLNetLARGE+Fine-tune</td><td colspan="2">100</td><td/><td/><td/><td/><td/><td/><td colspan="4">XLNetLARGE</td><td/><td colspan="5">XLNetLARGE+Fine-tune</td></tr><tr><td>Accuracy (%)</td><td>40 60 80</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>Accuracy (%)</td><td>40 60 80</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td>20</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>20</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td>0</td><td>Necessary Assumptions</td><td>Sufficient Assumptions</td><td>Strengthen</td><td>Weaken</td><td>Evaluation</td><td>Implication</td><td>Conclusion/Main Point</td><td>Most Strongly Supported</td><td>Explain or Resolve</td><td>Principle</td><td>Dispute</td><td>Technique</td><td>Role</td><td>Identify a Flaw</td><td>Match Flaws</td><td>Match Structures</td><td>Others</td><td/><td>0</td><td>Necessary Assumptions</td><td>Sufficient Assumptions</td><td>Strengthen</td><td>Weaken</td><td>Evaluation</td><td>Implication</td><td>Conclusion/Main Point</td><td>Most Strongly Supported</td><td>Explain or Resolve</td><td>Principle</td><td>Dispute</td><td>Technique</td><td>Role</td><td>Identify a Flaw</td><td>Match Flaws</td><td>Match Structures</td><td>Others</td></tr></table>

Peer review (iclr2020):
This paper presents a new reading comprehension dataset for logical reasoning. It is a multi-choice problem where questions are mainly from GMAT and LSAT, containing 4139 data points. The analyses of the data demonstrate that questions require diverse types of reasoning such as finding necessary/sufficient assumptions, whether statements strengthen/weaken the argument or explain/resolve the situation. The paper includes comprehensive experiments with baselines to identify bias in the dataset, where the answer-options-only model achieves near half (random is 25%). Based on this result, the test set is split into the easy and hard set, which will help better evaluation of the future models. The paper also reports the numbers on the split data using competitive baselines where the models achieve low performance on the hard set. Strengths 1) The introduced dataset is timely and is more challenging than existing reading comprehension datasets, based on the examples presented in the paper. 2) Analyses of reasoning types (Table 2) are done in a very comprehensive way, especially because they are comparable with descriptions from previous literature. This analysis clearly demonstrates that this data requires diverse types of challenging reasoning. 3) The experiments are comprehensive with many competitive baseline models, and their efforts to identify bias and use the result to split the test set are impressive. Weaknesses 1) Although the main claim of this paper is about logical reasoning, I believe the term “logical reasoning” is somewhat subjective. What exactly is the definition of logical reasoning? The paper never defines it in any way; it only shows Table 1 as an example. 2) (Continuing the point above) The paper mentions other datasets’ statistics on logical reasoning (in Section 1, they mention “0% in MCTest dataset and 1.2% SQuAD” requires logical reasoning). How was this analysis done? 3) (Continuing the point above) Are all types of reasoning in Table 2 logical reasoning? I agree that those reasoning are very challenging, but I am not convinced why some of them belong to logical reasoning (E.g. summary). I expect this problem can be resolved if authors bring a clearer definition of logical reasoning. 4) Although I appreciate the experiments to identify bias, I believe that the bias seems to be somewhat significant if 440 out of 1000 examples are identified as biased examples. I believe that the authors should do more comprehensive analysis on biased examples and explain what are possible biases here, in order to justify the significant bias. Marginal comments 1) The authors claim they collected the questions from open websites and books, but they didn’t provide the URLs or names. More information about which source was used, how data points were filtered and so on would be necessary to assess the quality of the dataset. Specifically, is there any particular reason for removing one of wrong options to make it have four choices, instead of five choices? 2) Why is the number of Chance model 0.39? Shouldn’t it be 1000 * 0.39% = 3.9? 3) In Table 5, it’s interesting to see that different models identify different numbers of biased examples. Have you looked at how much do they overlap each other if you take a pair of models? (A number of the union kinda shows it, but I wonder if there is a more intuitive way to see how consistent the models’ decisions are.) 4) It would be also helpful to see the baseline results of “question & answer options only model” and “context & answer options only model”.