Large-scale Cloze Test Dataset Designed by Teachers

# Large-scale Cloze Test Dataset Designed by Teachers

Qizhe Xie, Guokun Lai11footnotemark: 1, Zihang Dai, Eduard Hovy
Language Technologies Institute
Carnegie Melon University
{qizhex, guokun, dzihang, hovy}@cs.cmu.edu
equal contribution
###### Abstract

Cloze test is widely adopted in language exams to evaluate students’ language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH111CLOTH (CLOze test by TeacHers) is available at http://www.qizhexie.com/data/CLOTH.tar.gz., in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets. We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck.

Large-scale Cloze Test Dataset Designed by Teachers

Qizhe Xiethanks: equal contribution, Guokun Lai11footnotemark: 1, Zihang Dai, Eduard Hovy
Language Technologies Institute
Carnegie Melon University
{qizhex, guokun, dzihang, hovy}@cs.cmu.edu

## 1 Introduction

Being a classic language exercise, the cloze test (Taylor, 1953) is an accurate assessment of language proficiency (Fotos, 1991; Jonz, 1991; Tremblay, 2011) and has been widely employed in language examinations. Under standard setting, a cloze test requires examinees to fill in the missing word (or sentence) that best fits the surrounding context. To facilitate natural language understanding, automatically generated cloze datasets were introduced to measure the ability of machines in reading comprehension (Hermann et al., 2015; Hill et al., 2016; Onishi et al., 2016). In these datasets, each cloze question typically consists of a context paragraph and a question sentence. By randomly replacing a particular word in the question sentence with a blank symbol, a single test case is created. For instance, the CNN/Daily Mail (Hermann et al., 2015) take news articles as the context and the summary bullet points as the question sentence. Only named entities are considered when creating the blanks. Similarly, in Children’s Books test (CBT) (Hill et al., 2016), the cloze question is obtained by removing a word in the last sentence of every consecutive sentences, with the first 20 sentences being the context. Different from the CNN/Daily Mail datasets, CBT also provides each question with a candidate answer set, consisting of randomly sampled words with the same part-of-speech tag from the context as that of the ground truth.

Thanks to the automatic generation process, these datasets can be very large in size, leading to significant research progress. However, compared to how humans would create cloze questions, the automatic generation process bears some inevitable issues. Firstly, the blanks are chosen uniformly without considering which aspect of the language phenomenon the question will test. Hence, quite a portion of automatically generated questions can be purposeless or even trivial to answer. Another issue involves the ambiguity of the answer. Given a context and a blanked sentence, there can be multiple words that fit almost equally well into the blank. A possible solution is to include a candidate option set, as done by CBT, to get rid of the ambiguity. However, automatically generating the candidate option set can be problematic since it cannot guarantee the ambiguity is removed. More importantly, automatically generated candidates can be totally irrelevant or simply grammatically unsuitable for the blank, resulting in again trivial questions. Probably due to these unsatisfactory issues, it has been shown neural models have achieved comparable performance with human within very short time (Chen et al., 2016; Dhingra et al., 2016; Seo et al., 2016). While there has been work trying to incorporate human design into cloze question generation (Zweig & Burges, 2011), the MSR Sentence Completion Challenge created by this effort is quite small in size, limiting the possibility of developing powerful neural models on it.

Motivated by the aforementioned drawbacks, we propose CLOTH, a large-scale cloze test dataset collected from English exams. Questions in the dataset are designed by middle-school and high-school teachers to prepare Chinese students for entrance exams. To design a cloze test, teachers firstly determine the words that can test students’ knowledge in vocabulary, reasoning or grammar; then replace those words with blanks and provide three candidate options for each blank. If a question does not specifically test grammar usage, all of the candidate options would complete the sentence with correct grammar, leading to highly confusing questions. As a result, human-designed questions are usually harder and are a better assessment of language proficiency.

To verify if human-designed cloze questions are difficult for current models, we train dedicated models as well as the state-of-the-art language model and evaluate their performance on this dataset. We find that the state-of-the-art model lags behind human performance even if the model is trained on a large external corpus. We analyze where the model fails compared to human. After conducting error analysis, we assume the performance gap results from the model’s inability to use long-term context. To verify this assumption, we evaluate humans’ performance when they are only allowed to see one sentence as the context. Our assumption is confirmed by the matched performances of model and human when given only one sentence. In addition, we demonstrate that human-designed data is more informative and more difficult than automatically generated data. Specifically, when the same amount of training data is given, human-designed training data leads to better performance. Additionally, it is much easier for the same model to perform well on automatically generated data.

## 2 CLOTH Dataset

In this section, we introduce the CLOTH dataset that is collected from English examinations, and study the assessed abilities of this dataset.

### 2.1 Data Collection and Statistics

We collected the raw data from three free websites in China that gather exams designed by English teachers. These exams are used to prepare students for college/high school entrance exams. Before cleaning, there are passages and questions. We perform the following processes to ensure the validity of the data: 1. We remove questions with inconsistent format such as questions with more than four options; 2. We filter all questions whose validity relies on external information such as pictures or tables; 3. Further, we delete duplicated passages; 4. On one of the websites, the answers are stored as images. We use two OCR softwares, tesseract and ABBYY FineReader, to extract the answers from images. We discard the question when results from the two softwares are different. After the cleaning process, we obtain a dataset of passages and questions.

Since high school questions are more difficult than middle school questions, we divided the datasets into CLOTH-M and CLOTH-H, which stand for the middle school part and the high school part. We split of the data for both the test set and the dev set. The detailed statistics of the whole dataset and two subsets are presented in Table 1.

### 2.2 Question Type Analysis

In order to evaluate students’ mastery of a language, teachers usually design tests so that questions cover different aspects of a language. Specifically, they first identity words in the passage that can examine students knowledge in vocabulary, logic or grammar. Then, they replace the words with blanks and prepare three incorrect but confusing candidate options to make the test non-trivial. A sample passage is presented in Table 2.

To understand the assessed abilities on this dataset, we divide questions into several types and label the proportion of each type of questions. We find that the questions can be divided into the following types:

• Grammar: The question is about grammar usage, involving tense, preposition usage, active/passive voices, subjunctive mood and so on.

• Short-term-reasoning: The question is about content words and can be answered based on the information within the same sentence.

• Matching/paraphrasing: The question is answered by copying/paraphrasing a word.

• Long-term-reasoning: The answer must be inferred from synthesizing information distributed across multiple sentences.

We sample passages in the high school category and the middle school category respectively. Each passage in the high school category has questions and each passage in the middle school category has questions. The types of the question are labeled on Amazon Turk. We pay $1 and$0.5 for high school passage and middle school passage respectively.

The proportion of different questions is shown in Table 3. We find that the majority of questions are short-term-reasoning questions, in which the examinee needs to utilize grammar knowledge, vocabulary knowledge and simple reasoning to answer the questions. Note that questions in middle school are easier since they have more grammar questions. Finally, only approximately of data needs long-term information, in which the long-term-reasoning questions constitute a large proportion.

## 3 Exploring Models’ Limits

In this section, we study if human-designed cloze test is a challenging problem for state-of-the-art models. We find that the language model trained on large enough external corpus could not solve the cloze test. After conducting error analysis, we hypothesize that the model is not able to deal with long-term dependencies. We verify the hypothesis by evaluating human’s performance when human only sees one sentence as the context.

### 3.1 Human and Model Performance

#### Supervised LSTM

To test the performance of RNN based supervised models, we train a bidirectional LSTM (Hochreiter & Schmidhuber, 1997) to predict the missing word given the context, with only labeled data. The implementation details are in Appendix A.1.

#### Language model

Language modeling and cloze test are similar since, in both tasks, a word is predicted based on the context. In cloze test, the context on both sides may determine the correct answer. Suppose is the missing word and , are the context. Although language model is trained to predict the next word only using the left context, to utilize the surrounding context, we could choose that maximizes the joint probability , which essentially maximizes the conditional likelihood . Therefore, language model can be naturally adapted to cloze test.

In essence, language model treats each word as a possible blank and learns to predict it. As a result, it receives more supervision than the supervised model trained on human-labeled questions. Additionally, it can be trained on a very large unlabeled corpus. Interested in whether the state-of-the-art language model can solve cloze test, we first train a neural language model on the training set of our corpus, then we test the language model trained on One Billion Word Benchmark (Chelba et al., 2013) (referred as 1-billion-language-model) that achieves a perplexity of  (Jozefowicz et al., 2016)555The pre-trained model is obtained from https://github.com/tensorflow/models/tree/master/research/lm_1b. To make the evaluation time tractable, we limit the context length to one sentence or three sentences.

#### Human performance

We measure the performance of Amazon Turkers on sampled questions when the whole passage is given.

The comparison is shown in Table  4. The language model trained on our dataset achieves an accuracy of while the supervised model’s accuracy is , indicating that more training data results in better generalization. When only one sentence is given as context, the accuracy of 1-billion-language-model is , which shows that the amount of data is an essential factor affecting the model’s performance. If we increase the context length to three sentences, the accuracy of 1-billion-language-model only improves to . In contrast, human outperforms 1-billion-language-model by a significant margin, which demonstrate that deliberately designed questions in CLOTH are not completely solved even for state-of-the-art models.

### 3.2 Analyzing model’s performance by human study

In this section, we would like to understand why the state-of-the-art model lags behind human performance.

We find that most of errors made by the large language model involve long-term reasoning. Additionally, in a lot of cases, the dependency is within the context of three sentences. Several errors made by the large language model are shown in Table 5. In the first example, the model does not know that Nancy found nobody in the company means that Nancy was the first one to arrive at the company. In the second and third example, the model fails probably because of the coreference from “they” to “flowers”. The dependency in the last case is longer. It depends on the fact that “Nancy” was alone in the company, .

Based on the case study, we hypothesize that the language model is not able to take long-term information into account, possibly due to the difficulty of long-term reasoning. Moreover, the 1-billion-language-model is trained on sentence level, which might also result in paying more attention to short-term information. However, we do not have enough computational resources to train a large model on 1 Billion Word Benchmark to investigate the differences of training on sentence level or on paragraph level.

An available comparison is to test the model’s performance on different types of questions. We find that the model’s accuracy is on long-term-reasoning while achieving on short-term-reasoning, which partially confirms that long-term-reasoning is harder. However, we could not completely rely on the performance on specific questions types, partly due to the small sample size. A more fundamental reason is that the question type labels are subjective and their reliability depends on whether turkers are careful enough. For example, in the error analysis show in Table 5, a careless turker would label the second example as short-term-reasoning without noticing that the meaning of “they” relies on a long context span.

To objectively verify if the language model’s strengths are in dealing with short-term information, we obtain the ceiling performance of only utilizing short-term information. Showing only one sentence as the context, we ask the turkers to label all possible options that they deem to be correct given the insufficient information. We also ask them to select a single option based on their best guesses. By limiting the context span manually, the ceiling performance with only the access to short context is estimated accurately.

The performances of turkers and 1-billion-language-model are shown in Table 6. The performance of 1-billion-language-model using one sentence as the context can almost match the ceiling performance of only using short-term information. Hence we conclude that the language model can almost perfectly solve all short-term cloze questions. However, the performance of language model is not improved significantly when the needed long-term context is given, indicating that the performance gap is due to the inability of long-term reasoning.

The human study on short-term ceiling performance also reveals that the options are carefully picked. Specifically, when a turker thinks that a question has multiple answers, out of options are deemed to be possibly correct, which means that teachers design the options so that three or four options all make sense if we only look at the local context.

## 4 Comparing Human-designed data and Automatically-Generated Data

In this section, we compare human-designed data and automatically generated data through extensive experiments. We demonstrate that human-designed data is more informative, i.e., it provides more valuable supervision signals. We also show that human-designed data is more difficult since the deleted words and candidate options are carefully chosen by teachers.

### 4.1 Informativeness Comparison

At a casual observation, a cloze test can be created by randomly deleting words and randomly sampling candidate options. In fact, to leverage large-scale data, similar generation processes have been introduced and widely used in machine comprehension  (Hermann et al., 2015; Hill et al., 2016; Onishi et al., 2016). However, researches on cloze test design (Sachs et al., 1997) show that tests created by deliberately deleting words are more reliable than tests created by randomly or periodically deleting words. To design accurate language proficiency assessment, teachers usually selects words in order to examine students’ proficiency in grammar, vocabulary and reasoning. Moreover, in order to make the question non-trivial, the other three incorrect options provided by teachers are usually grammatically correct and relevant to the context. For instance, in the fourth problem of the sample passage shown in Table 2, “grapes”, “flowers” and “bananas” all fit the description of being fresh. We know “flowers” is the correct answer after seeing the sentence “Somebody has sent me flowers the very first day!”.

Naturally, we hypothesize that human-generated data is more informative than randomly generated data. In other words, human-generated data provides more valuable supervision signals for a system to understand the complexity of human language, which is reflected by the carefully chosen deleted words and candidate options. To verify this assumption, we train a model on the following generated data while keeping the amount of training data the same:

• Random-options: We replace the candidate options picked by teachers with random words sampled by the unigram distribution.

• Random-blanks: We further replace the deleted words chosen by teachers with random words in the passage, while keeping the number of blanks the same. The candidate options are also automatically generated.

We train an LSTM based supervised model with different training data, while keeping the same dev set and test set. The comparison is shown in Table 7. When trained with human-designed data, the accuracy is . If we replace the human-generated options with random options, the accuracy drops significantly to . When blanks are also selected randomly, the overall performance further drops to . Hence, the deleted words and options designed by human provide more knowledge of the language. Interestingly, it leads to better performance on CLOTH-M to train on “random-blanks” than to train on “random-options”. It further reflects the difficulty difference between middle school questions and high school questions, since middle school exams have more grammar questions involving simple words such as “in”, “at” and “on”, which usually do not appear in high school exams. We also find that, when trained on automatically generated data, the model’s training accuracy increases much faster and quickly saturates, which is also an indicator of being uninformative.

### 4.2 Combining Human-designed Data with automatically-generated data

In Section 3.1, we show that language model is able to take advantage of more supervisions since it predicts each word based on the context. In essence, each word provide an automatically-generated question. At the same time, we also show the benefits of training on human-designed informative data in Section 4.1. Motivated by the belief that advantages of high quality and large quantity are usually complementary, we combine these two types of data to achieve better performance.

Note that discriminative models can also treat all words in a passage as automatically-generated questions, just like a language model (Please see the Appendix A.3 for details). We study two methods of leveraging automatically-generated data and human-designed data:

#### Equally averaging

Let be the average loss for all human-designed questions and be the average loss for all automatically-generated questions in the passage. A straightforward method is to optimize so that the model learns to predict words deleted by human and all other words in the passage. We set to in our experiments. This model treats each automatically-generated questions as equally important.

#### Informativeness-based weighted averaging

A possible avenue towards having large-scale high-quality data is to automatically pick out informative questions in a large corpus. With the belief that human-designed data is informative, we train a network to predict the informativeness of each question by mimicking the design behavior of language teachers. The performance of the informativeness prediction network and an example is shown in Appendix A.4.

Let denotes the negative log likelihood loss for the th question and let be the outputted informativeness of the -th question (The definition of is in Appendix A.2). We define the informativeness weighted loss function as where is the set of all human-generated questions and is the temperature of the Softmax function. When the temperature is , the model degenerate into equally averaging objective function without using the informativeness. When the temperature is , only the most informative question is used. We set to based on the performance on the dev set.

We present the results in Table 8. When all other words are treated as equally important, the accuracy is , similar to the performance of language model. Informativeness-based weighted averaging leads to an accuracy of , better than achieved by equally averaging. When combined with human-designed data, the performance can be improved to 666The code is available at https://github.com/qizhex/Large-scale-Cloze-Test-Dataset-Designed-by-Teachers..

### 4.3 Difficulty Comparison

Lastly, we study if human-designed data is more difficult compared to automatically generated data. We employ the same generation process used in Section 4.1 to replace options and blanks and test the best model’s performance. As shown in Table 9, On automatically generated questions, our model achieves an accuracy of and , while its accuracy is on human-designed questions, which shows that automatically generated questions are much easier.

## 5 Related Work

Large-scale automatically generated cloze test (Hermann et al., 2015; Hill et al., 2016; Onishi et al., 2016) leaded to significant research advancement. However, the generated questions do not consider the language phenomenon to be tested and are relatively easy to solve. Recently proposed reading comprehension datasets are all labeled by human to ensure their qualities (Rajpurkar et al., 2016; Joshi et al., 2017; Trischler et al., 2016; Nguyen et al., 2016). Aiming to evaluate machines under the same conditions human is evaluated, there are a growing interests in obtaining data from examinations. NTCIR QA Lab (Shibuki et al., 2014) contains a set of real-world university entrance exam questions. The Entrance Exams task at CLEF QA Track (Peñas et al., 2014; Rodrigo et al., 2015) evaluates machine’s reading comprehension ability. The AI2 Elementary School Science Questions dataset provides scientific questions used in elementary and middle schools. Lai et al. (2017) proposes the first large-scale machine comprehension dataset obtained from exams. They show that questions designed by teachers have a significant larger proportion of reasoning questions. Our dataset focuses on evaluating language proficiency while the focus of reading comprehension is reasoning.

## 6 Conclusion

In this paper, we propose a large-scale cloze test dataset CLOTH that is designed by teachers. With the missing blanks and candidate options carefully created by teachers to test different aspects of language phenomenon, CLOTH requires a deep language understanding and better captures the complexity of human language. We find that human outperforms state-of-the-art models by a significant margin, even if the model is trained on a large corpus. After detailed analysis, we find that the performance gap is due to model’s inability to understanding a long context. We also show that, compared to automatically-generated questions, human-designed questions are both more informative and more difficult.

## Acknowledgement

We thank Yulun Du and Yichao Zhang for suggestions on the draft. We thank Shi Feng for the script to highlight informative words. We thank Kaiyu Shi and Zhilin Yang for insightful discussions. This research was supported in part by DARPA grant FA8750-12-2-0342 funded under the DEFT program.

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## Appendix A Appendix

### a.1 Implementation Details

We implement our models using PyTorch. The code of language model is adapted from the language model in PyTorch example projects. We use Adam (Kingma & Ba, 2014) with the learning rate of . The hidden dimension is set to and we initialize the word embedding by -dimensional Glove word vector (Pennington et al., 2014). The temperature is set to . We train our model on all questions in CLOTH and test it on CLOTH-M and CLOTH-H separately.

### a.2 Informativeness Prediction Network

Let denote the passage and denote whether a word is selected as a question by human, i.e., is if this word is selected to be filled in the original passage or otherwise. Suppose is the representation of -th word given by a bidirectional LSTM. The network computes the probability of being a question in the cloze test as follows:

 li=hTiwe;pi=Sigmoid(li)

where is the logit or the energy, indicating whether this word is likely to be a problem selected by human instructors. We train the network to minimize the binary cross entropy between and ground-truth labels at each token.

### a.3 Using all words as automatically-generated questions

The passage is encoded by a bidirectional LSTM. Let be the context representation at -th word. To mask the word , is defined as , where and are the hidden representations of forward LSTM and backward LSTM respectively. Suppose there are candidate words at position , we denote the cross entropy loss between the model prediction and the ground-truth as,

 qi =Softmax(eTwi,1Whi,eTwi,2Whi,⋯,eTwi,mWhi) Ji =Cross\_Entropy(yi,qi)

where is the embedding of the word and is a weight matrix. In human-designed questions, there is a list of candidate options for each question. For automatically-generated questions, the candidate options include the whole vocabulary, in which case is equal to the vocabulary size.

### a.4 Performance of the Informativeness Prediction Network

A predicted sample is shown in Figure 1101010The script to generate the Figure is obtained at https://gist.github.com/ihsgnef/f13c35cd46624c8f458a4d23589ac768. Clearly, words that are too obvious have low scores, such as punctuation marks, simple words “a” and “the”. In contrast, content words whose semantics are directly related to the context have a higher score, e.g., “same”, “similar”, “difference” have a high score when the difference between two objects is discussed and “secrets” has a high score since it is related to the subsequent sentence “does not want to share with others”.

Our prediction model achieves an F1 score of on the test set, which is understandable since there are many plausible questions within a passage.

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