| # A Comparative Analysis of the Effectiveness of Rare Tokens on Creative Expression using ramBERT |
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| Youbin Lee, Deokgi Kim, Byung-Won On |
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| Kunsan National University |
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| {hanbin0694, thekey1220, bwon}@kunsan.ac.kr |
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| Ingyu Lee |
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| Yeungnam University |
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| inlee3941@gmail.com |
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| # Abstract |
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| Until now, few studies have been explored on Automated Creative Essay Scoring (ACES), in which a pre-trained model automatically labels an essay as a creative or a non-creative. Since the creativity evaluation of essays is very subjective, each evaluator often has his or her own criteria for creativity. For this reason, quantifying creativity in essays is very challenging. In this work, as one of preliminary studies in developing a novel model for ACES, we deeply investigate the correlation between creative essays and expressiveness. Specifically, we explore how rare tokens affect the evaluation of creativity for essays. For such a journey, we present five distinct methods to extract rare tokens, and conduct a comparative study on the correlation between rare tokens and creative essay evaluation results using BERT. Our experimental results showed clear correlation between rare tokens and creative essays. In all test sets, accuracies of our rare token masking-based BERT (ramBERT) model were improved over the existing BERT model up to $14\%$ . |
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| # 1 Introduction |
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| In the era of the Fourth Industrial Revolution, new knowledge creation based on existing knowledge is becoming more important than any other field. With the advent of Artificial Intelligence (AI) technology, which is the core of this industrial revolution, AI takes charge of simple repetitive tasks and allow humans to focus more on creative activities. So that, new innovations can be achieved through collaboration between humans and AI. This knowledge creation is based on human creative thinking (Noh and Kim, 2008) |
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| One of the representative creative thinking activities is writing. Creative texts are new and at the same time communicable to readers within social and cultural contexts. Creative writing is the act of writing creative texts. Specifically, it is defined as a writing activity in which writers express their |
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| new and original ideas so that they can be communicated appropriately and effectively in social and cultural contexts. In this sense, through creative writing, middle and high school students can develop their ability for creativity that is essential for the Fourth Industrial Revolution. |
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| Creativity is a human mental activity, known as a complex characteristic that is difficult to explain. In addition, it is not limited to one academic field, but the nature of creativity varies slightly depending on the disciplines in question, such as linguistics, psychology, and literature. Academically, there is no clear definition of creativity yet. |
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| Looking at the discussion so far, it has been defined differently depending on which part of creativity the researcher has focused on. For example, (Torrance, 1974) defined creativity as "the process of becoming sensitive to problems, flaws, gaps in knowledge, missing elements, and incongruities", (Sternberg and Lubart, 1999) as "generating new and useful ideas," and (Plucker et al., 2004) as "the interaction between processes and capacities that produce novel and useful outputs within a specific social context." To date, the most widely accepted definition of creativity by many researchers is "an individual's ability to create something new and appropriate." |
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| Table 1 summarizes the creativity evaluation metric used to evaluate actual creative writing. Creativity in writing is largely divided into creativity in process and creativity in outcome. The former refers to the cognitive process leading to text production, and the latter refers to creativity evaluation through the resulting text. Since computational creativity mainly focuses on the latter rather than the former, we also focus on the latter in this work. |
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| As shown in the table, the creativity in outcome is divided into creativities in expression and content. Because clearly evaluating the creativity in content is implausible with current technology, we focus now on assessing the creativity in expression while |
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| <table><tr><td>Main category</td><td>Middle category</td><td>Subdivision</td><td>Creativity evaluation index</td></tr><tr><td rowspan="8">Creativity in Writing</td><td rowspan="4">Creativity in Process</td><td></td><td>Fluency</td></tr><tr><td></td><td>Flexibility</td></tr><tr><td></td><td>Originality</td></tr><tr><td></td><td>Elaboration</td></tr><tr><td rowspan="4">Creativity in Outcome</td><td>Creativity in Expression</td><td>Originality</td></tr><tr><td rowspan="3">Creativity in Content</td><td>Appropriateness</td></tr><tr><td>Originality</td></tr><tr><td>Appropriateness</td></tr></table> |
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| Table 1: Creativity evaluation metric. |
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| leaving the creativity in content as a topic for our future research. |
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| The creativity in expression has two common creativity evaluation indices. One is originality and the other is appropriateness. Technically, the originality is the ability to produce new and unique expressions that are different from existing ones, and the appropriateness must include all the requirements of 'good writing', such as having to strictly follow the grammatical rules and, in any case, having to meet the requirements appropriate for the given context. |
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| In order to quantify the appropriateness index, various Automated Essay Scoring (AES) models that take essays as input are actively developed in the field of natural language processing in recent years. We will introduce the state-of-the-art AES models in detail in Section 2. Please note that studying the appropriateness index is out of the scope of this work. |
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| In this paper, we focus only on the originality index in the creativity in expression. Throughout this paper, the expression includes the tokens of subword, word, $n$ -gram, and span(phrase or sentence). Furthermore, to clearly quantify the originality index, as we already mentioned, we first consider it to be new and unique tokens within the given corpus, but the meaning of "new and unique" is ambiguous. In our second thought, we define it as rare tokens in the corpus. |
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| The goal of our research is to deeply investigate the correlation between rare tokens and creativity assessment. In the corpus, rare tokens can be extracted through various methods such as Byte Pair Encoding (BPE), Inverse Document Frequency (IDF), Clustering in latent semantic spaces, SpanBERT, and existing rare word dictionaries including Stanford Rare Word (Luong et al., 2013), Cambridge Rare Word (Pilehvar et al., 2018), Contextual Rare Word (Khodak et al., 2018), and Definitional Nonce (Herbelot and Baroni, 2017). |
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| In our framework, Bidirectional Encoder Representations from Transformers (BERT) is pre |
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| trained with Masked Language Model (MLM). Unlike the existing BERT model, rare words rather than random words are masked and predicted in our model. Then, in the fine-tuning step, the pretrained BERT model learns with the training set of essays. |
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| Our contributions are the followings: |
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| - To the best of our knowledge, we are the first to deeply study the correlation between rare tokens and creativity assessment in the automated essay scoring problem. |
| - We present how to extract rare tokens in various approaches: BPE, IDF, Clustering in latent semantic spaces, SpanBERT, and existing rare word dictionaries such as Stanford Rare Word, Cambridge Rare Word, and Contextual Rare Word. |
| - We built and validated a training set including 800 creative essays with the help of linguistics experts from the Automated Student Assessment Prize (ASAP) dataset (ASAP, 2022). Our experimental results show that all accuracies of the pre-trained BERT model with rare tokens have been improved up to $14\%$ in assessing creativity in essays compared to the existing BERT model. |
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| # 2 Related Work |
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| The AES researches have been focused on generating hand-crafted features as an input of classification or regression (Larkey, 1998; Rudner and Liang, 2002; Attali and Burstein, 2006; Yannakoudakis et al., 2011; Chen and He, 2013; Phandi et al., 2015; Cozma et al., 2018). The linguistics features such as style and grammar are used (Lu et al., 2017; Ramalingam et al., 2018; Chen and He, 2013; Phandi et al., 2015). Sometimes, we analyze the contents by Latent semantic analysis (Ratna et al., 2007; Amalia et al., 2019; Ratna et al., 2018, 2019a,b; Shehab et al., 2018; Ratna et al., 2019c, 2015; Kakkonen et al., 2005; Xu et al., 2017), by WordNet (Al Awaida et al., 2019) and word embedding vectors (Dong and Zhang, 2016), by using specific language features (Wong and Bong, 2019; Cheon et al., 2015) and Artificial Neural Networks (Nguyen and Dery, 2016; Taghipour and Ng, 2016; Liang et al., 2018). We also hybrid the style and content analysis to improve further (Ishioka and Kameda, 2006; Peng et al., 2010; Imaki et al., |
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| 2013; Alghamdi et al., 2014; Jin and He, 2015; Al-Jouie and Azmi, 2017; Contreras et al., 2018). |
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| With the advent of deep learning technologies, AES have improved by using the pre-trained models with large data set (Taghipour and Ng, 2016; Dong and Zhang, 2016; Dong et al., 2017; Wang et al., 2018; Tay et al., 2018; Farag et al., 2018; Song et al., 2020; Ridley et al., 2021; Muangkammuen and Fukumoto, 2020; Mathias et al., 2020; Jin et al., 2018; Dasgupta et al., 2018). The LSTM and RNN are naturally choices for AES task and some researchers applied BERT for the task. BERT based approaches (Uto et al., 2020; Rodriguez et al., 2019; Mayfield and Black, 2020) shows an inferior performance than LSTM (Dong et al., 2017; Tay et al., 2018) in general. However, Cao et al. (2020) and Yang et al. (2020) are knowns to show a compatible performance to LSTM based systems even with BERT. Other variations are Song et al. (2020) used a transfer learning to overcome the size limitation of training data, Wu et al. (2021) applied the R-Drop to avoid the overfitting, and Wang et al. (2022) used a transfer learning with multi-scale essay representations with BERT. |
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| On the other hands, AES system has been studied as in a novelty or creativity detection perspective. Liang et al. (2021) proposed a model to detect creative essay using a Generative Adversarial Networks on the ASAP data. Doboli et al. (2020) used a cognitive inspired approach to detect novel ideas on short text. Chikkamath et al. (2020) applied the machine learning and deep learning approaches with various embedding vectors to find a new technology on patent data. Bhattarai et al (2020) proposed a Tsetline machine to detect a novel text using conjunctive clauses. Nandi and Basak (2020) proposed several CNN architectures to detect novel texts. Beaty and Johnson (2021) proposed an open platform to detect creativity based on semantic distances on word embeddings. Amplayo et al. (2019) evaluated the academic research paper novelty detection using time and impact simulations. Simpson et al. (2019) proposed Bayesian approach to predict humor and metaphor score using Gaussian process preference learning. Christophe et al. (2021; 2020) proposed a framework to detect a new topic by monitoring the geometrical properties of word embeddings. Ghosal et. al. proposed relative document vector based CNN model (Ghosal et al., 2018a) and a TAP-DLIND benchmark data sets (Ghosal et al., 2018b, 2022). |
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| Many researches have been done in detecting creative essays as mentioned in this section. However, as authors aware, this is the first attempt to use the low frequency words to detect creative essays. Since the rare words are highly correlated with creative essays, we conjecture that the proposed approach will show a promising improvement. |
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| # 3 Methodology |
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| The ultimate goal of our study is to understand how strongly a pre-trained encoder model like BERT for ACES is affected by rare tokens. |
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| To do this, given a large-sized set of text documents, we first extract a list of rare tokens using various approaches that we will discuss in detail in Section 3.1. Then, we will explain in detail our framework for comparative analysis of ACES in Section 3.2. Finally, in Section 3.3, we will design main questions for data-driven insights we want to know about through this study. |
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| # 3.1 Extraction of Rare Tokens |
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| Since our research focuses on creativity in expression, we consider various types of tokens as the expression. A token $t_i^*$ is one of subword $(t_i^s)$ , word $(t_i^w)$ , n-gram $(t_i^n)$ , and span $(t_i^S)$ that corresponds to a phrase, clause, or even sentence. In the case of $n$ -gram tokens, to distinguish them from spans, 2-grams are used in this study. This is, $t_i^* \in \{t_i^s, t_i^w, t_i^{n=2}, t_i^S\}$ . For example, in the pre-training step for BERT, when rare tokens are masked in a sentence like "we do not want to squander privileges and our essential things", $t_i^s$ are 'sq', '#uan', and '#der'; $t_i^w$ is 'squander'; $t_i^{n=2}$ is 'squander privileges'; and $t_i^S$ is 'squander privileges and our essential things'. |
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| A set of rare tokens $\Phi_i = \{r_1, r_2, \dots, r_m\}$ is created through a method $f_i \in F = \{f_1, f_2, f_3, f_4, f_5\}$ from a corpus of large text documents $C = \{d_1, d_2, \dots, d_n\}$ used to pre-train the BERT model. For a comparative study, we create a total of 7 sets of rare tokens to see if there is a correlation between creative evaluation results and rare tokens. Those sets are $\Phi_{i=1 \sim 7}$ . |
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| The first set of rare tokens is constructed as defined in Eq. 1. |
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| $$ |
| \Phi_ {1} = \left\{r _ {i} \mid r _ {i} = f _ {1} (x) \wedge r _ {i} = t _ {i} ^ {s} \right\} \tag {1} |
| $$ |
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| , where $x$ is a word token. $f_{1}$ is one of subword-based tokenizers. There are various to- |
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| kenizers such as Byte-Pair Encoding (BPE), Word Piece Model (WPM), Unigram, and Sentence Piece Model (SPM), but we use BPE in this work. As the number of vocabularies increases in the pre-trained model, the dimension of word embedding vectors increases or the model becomes more complex. Therefore, units are used instead of vocabularies to reduce the number of vocabularies. A unit is a group of frequently appearing characters in a corpus and refers to a word or subword. A common word such as 'makers' and 'over' is set as one unit because it appears frequently in the corpus, while 'jet' is a rare word, so it is divided into 'j' and 'et' units. For example, in a sentence like "jet makers feud over seat width with big orders at stake", the units are $\{\mathrm{j},$ et, makers, fe, ud, over, seat, width, with, big, orders, at, stake\}. |
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| In the initial time, function $f_{1}$ tokenizes a given corpus. For example, {('hug', 10), ('pug', 5), ('pun', 12), ('bun', 4), ('hugs', 5)}, where each parentheses has a token and its frequency. After splitting words into characters using a pre-defined dictionary such as ['b', 'g', 'h', 'n', 'p', 's', 'u'], we get the same result as {('h' 'u' 'g', 10), ('p' 'u' 'g', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'u' 'g' 's', 5)}. In this result, the most frequently appearing character pairs are selected. For instance, the frequency of 'hu' is 15, while that of 'ug' is 20. Since 'ug' has the highest frequency, it is newly added to the dictionary - i.e., ['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug']. This process is repeated until the number of times $i$ specified by the user. If $i = 3$ , the final dictionary includes units in ['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']. Finally, we consider the top- $k$ units with the lowest frequency as rare tokens $t_{i}^{s}$ . |
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| The second set of rare tokens is created as defined in Eq. 2. |
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| $$ |
| \Phi_ {2} = \left\{r _ {i} \mid r _ {i} = f _ {2} (x) \wedge r _ {i} = t _ {i} ^ {w} \right\} \tag {2} |
| $$ |
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| , where function $f_{2}$ decides if $x$ is a rare word or not. To implement $f_{2}$ , we use Inverse Document Frequency (IDF) that assigns a high score to a word that appears infrequently in the corpus, assuming it is an important word. For example, proper nouns such as 'biden' and 'google' have high values and stopwords such as 'in' and 'the' have low scores. For example, suppose that the number of documents in a corpus is one million and that of documents that contain 'biden' is one |
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| Figure 1: Overall concept of clustering contextualized vectors in the semantic space. |
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| thousand. The IDF value of 'biden' $f_{2}(\text{'biden'}) = 1 + \log \left( \frac{1,000,000}{1,000} \right) = 4$ . As a final, we select the top- $k$ words with the highest IDF values to use rare words for pre-training rare word-based masked language model in BERT. |
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| The third set of rare tokens is created as defined in Eq. 3. |
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| $$ |
| \Phi_ {3} = \left\{r _ {i} \mid r _ {i} = f _ {3} (x) \wedge r _ {i} = t _ {i} ^ {w} \right\} \tag {3} |
| $$ |
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| , where function $f_{3}$ performs two steps. In the first step, the contextualized vector $v$ corresponding to each word $x$ is obtained using the pre-trained BERT and is projected into the latent semantic space. This is, $f_{BERT}(x) = v$ . In the second step, all contextualized vectors are clustered in the semantic space. Since we do not know in advance how many clusters exist in the semantic space, we must use one of unsupervised clustering methods. In this work, we use Expectation-Maximization (EM), in which clustering is performed by calculating the probability of points generated by $k$ Gaussian mixture models. In Expectation step (E-step), compute $P(C_{j}|v_{i}) = \frac{P(C_{j})P(v_{i}|C_{j})}{\Sigma_{l=1}^{k}P(C_{l})P(v_{i}|C_{l})}$ . In Maximization step (M-step), for every cluster (e.g., a cluster $C_{j}$ ), update the weight, mean, and standard deviation of the cluster by $P(C_{j}) = \frac{1}{n}\Sigma_{i=1}^{n}P(C_{j}|v_{i})$ , $\mu_{C_{j}} = \frac{\Sigma_{i=1}^{n}(v_{i} - \mu_{C_{j}})^{2}P(C_{j}|v_{i})}{\Sigma_{i=1}^{n}P(C_{j}|v_{i})}$ , and $\sigma_{C_{j}} = \frac{\Sigma_{i=1}^{n}(v_{i} - \mu_{C_{j}})^{2}P(C_{j}|v_{i})}{\Sigma_{i=1}^{n}P(C_{j}|v_{i})}$ , using $P(C_{j}|v_{i})$ updated in E-step. |
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| Figure 1 illustrates the output of $f_{3}$ . There exist three clusters of contextualized vectors. For convenience, we denote the clusters as green, blue, and red clusters. The red cluster has a relatively small cluster size compared to the green and blue clusters. This means that the word vectors in the green |
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| and blue clusters are related to common topics and expressions. Words corresponding to such vectors are likely to appear frequently in a corpus. On the other hand, words belonging to the red cluster are relatively likely to be rare words in the corpus. Therefore, to extract rare words through $f_{3}$ , we focus on the smallest cluster $C_{s}$ (the red cluster in Figure 1). Finally, we select only words corresponding to the contextualized vectors belong to $C_{s}$ . Assuming that there are three clusters $C_{1}, C_{2}$ and $C_{3}$ , where $C_{s} = C_{1}$ , all selected words must satisfy Eq. 4. |
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| $$ |
| \{v \mid v \in C _ {s} \wedge v \notin \left(C _ {2} \cup C _ {3}\right) \} \tag {4} |
| $$ |
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| The fourth set $\Phi_4$ is the union of sets $\Phi_2$ and $\Phi_3$ . In general, rare tokens in $\Phi_2$ are extracted in terms of lexical representation, while those from $\Phi_3$ are extracted in terms of semantic representation. Therefore, if BERT is pre-trained by rare word-based mask language model using $\Phi_4$ , we can know how rare words obtained by both representation approaches affect the creativity evaluation of essays. In addition, the fifth set $\Phi_5$ is similar to $\Phi_3$ except using $n$ -grams instead of words. We use 2 for $n$ in our experiments. For instance, in a sentence like "we do not want to squander", in the first step of $f_3$ , $f_{BERT}(x) = v$ , where $x = \text{we do}$ , 'do not', 'not want', 'want to', or 'to squander' and the second step is the same. See Eq. 5. |
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| $$ |
| \Phi_ {5} = \left\{r _ {i} \mid r _ {i} = f _ {3} (x) \wedge r _ {i} = t _ {i} ^ {n = 2} \right\} \tag {5} |
| $$ |
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| We create the sixth set $\Phi_6$ through function $f_{4}$ as shown in Eq. 6. |
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| $$ |
| \Phi_ {6} = \left\{r _ {i} \mid r _ {i} = f _ {4} (x) \wedge r _ {i} = t _ {i} ^ {S} \right\} \tag {6} |
| $$ |
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| , where $x$ is a sequence of words that is the input of $f_4$ . Unlike the functions $f_1, f_2$ , and $f_3$ , it identifies whether a span of text is rare in a corpus. As we already discussed, creative expressions can be clauses, phrases, and even sentences, as well as subwords or words. As $x$ is a sequence of words, we attempt to find a span of $x$ that corresponds to a clause, a phrase, or a sentence. To present $f_4$ , we first use SpanBERT to represent and predict spans of text, training span boundary representations to correctly predict masked span. The final loss function of SpanBERT $L(x_i)$ is to sum the losses from both Span Boundary Objective (SBO) and Masked Language Model (MLM) |
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| Objective for each token $x_{i}$ in the masked span $(x_{s},\dots,x_{e})$ , where $x_{s}$ and $x_{e}$ are the boundary tokens. $L(x_{i}) = L_{MLM}(x_{i}) + L_{SBO}(x_{i}) = -\log P(x_{i}|x_{i}) - \log P(x_{i}|x_{s},x_{e},p_{|p(x_{i}) - p(x_{s})|})$ , where $p_{|p(x_i) - p(x_s)|}$ is the relative position between $x_{i}$ 's position $p(x_{i})$ and $x_{i}$ 's position $p(x_{s})$ . See (Joshi et al., 2020) for details. After finding spans through SpanBERT, we use $f_{3}$ in order to detect rare spans in the corpus. |
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| The last set of rare tokens is $\Phi_7$ , as defined in Eq. 7. |
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| $$ |
| \Phi_ {7} = \left\{r _ {i} \mid r _ {i} = f _ {5} (x) \wedge r _ {i} = t _ {i} ^ {w} \right\} \tag {7} |
| $$ |
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| Recently, several dictionaries such as Stanford Rare Word, Cambridge Rare Word, Contextual Rare Word, and Definitional Nonce have been open in public. The goal of constructing such dictionaries is to get good embedding vectors from a given corpus, using Word2Vec. The most drawback of existing word embedding models is that frequent words in the corpus can generate good embedding vectors, but not for rare and unseen words. To address this problem, advanced word2vec models such as Morphological Recursive Neural Network (morphoRNN) and Neural Language Model (NLM) (Luong et al., 2013), a linear transformation of additive model $v_{w}^{additive} = \frac{1}{|\Gamma_{w}|}\sum_{\gamma \in \Gamma_{w}}\frac{1}{|\gamma|}\Sigma_{w\in \gamma}v_{w}$ , where $\Gamma_w$ is a context that contains word $w$ , have been presented in NLP. |
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| The function $f_{5}$ is to use one of rare word dictionaries. If $x$ matches a rare word in a dictionary, $x$ is added to $\Phi_7$ . For our experiment, we use the Harvard dictionary about rare words collected by Context-sensitive morphoRNN, which is the most representative one in this area. |
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| # 3.2 ramBERT |
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| In this section, we present our framework called ramBERT for comparative analysis of ACES. Figure 2 depicts the ramBERT model. First, we modify the masked language model of the existing BERT model in which rare tokens extracted through $f_{i} \in F$ discussed in Section 3.1 are masked and predicted in the pre-training step. Unlike existing BERT models, through the language model of ramBERT that masks and predicts rare tokens correctly, it is likely to attend more over rare tokens than over common tokens in texts. Next, the pre-trained BERT model is trained with a training set of essays in the fine-tuning step. In the final step, it automatically classifies each essay in a test |
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| Figure 2: ramBERT: Our framework for comparative analysis of Automated Creative Essay Scoring (ACES). |
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| set to be either creative and non-creative. |
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| # 3.3 Main Questions |
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| In this work, we will investigate the correlation between rare tokens and creative essay evaluation results. If there is any correlation, we will also examine how different types of rare tokens such as subwords, words, $n$ -gram tokens, and spans affect creative essay evaluation. In addition, we plan to look into which type of rare token has the greatest impact on creativity assessment. Furthermore, we will find out which of $F = \{f_1, f_2, f_3, f_4, f_5\}$ for extracting rare tokens is the most effective in evaluating essays for creativity. Please, note that we now have seven sets of rare tokens $\Phi_{i=1\sim7}$ . We will measure the degree of overlap of rare tokens between two sets in order to see how similar they are to each other. We will also investigate how creativity evaluation results change as the percentage of masked rare tokens is gradually increased. |
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| # 4 Experimental Set-up |
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| For the experiment, we first implemented the five rare token extraction methods. We wrote Python script codes to implement $f_{1}$ using BERTokenizer of Hugging Face, $f_{2}$ and $f_{3}$ using scikit-learn 1.2.0, $f_{4}$ using SpanBERT base model (uncased) in PyTorch, and $f_{5}$ using Stanford, Cambridge, and Contextual Rare Word dictionaries. We also modified TensorFlow code of BERT base model (uncased) for implementing the rare token-based masked language model. |
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| A total of 4,079,432 documents from Wikipedia were collected and text was extracted by removing html tags in each document. Such refined text was used as input for BERT's pre-training. To train ramBERT, we used the same default parameters as the BERT base model, where we set 32 to batch size, 10 to epoch, 2e-5 to learning rate, and 0.1 to |
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| dropout rate. In addition, we used Adam optimizer and we set 128 to maximum sequence length, 20 to maximum number of predictions per sequence, and 0.1 to masked language mode probability. |
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| To fine-tune ramBERT and perform the downstream task, we constructed a training set for creative essay assessment. First, we selected 800 essays at random from Prompt 1 of the ASAP dataset (ASAP, 2022). The topic of the essays is how computers affect people. In the existing ASAP dataset, the essay score ranges from 2 to 12 points, and the higher the essay score, the better the writing, regardless of creativity. Then, each essay was labelled as creative or non-creative by three domain experts who voted to classify each essay as either of creative or non-creative labels. |
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| All models were in standalone executed in a high-performance workstation server with Intel Xeon Scalable Silver 4414 2.20GHz CPU with 40 cores, 24GB RAM, 1TB SSD, and GEFORCE RTX 3080 Ti D6 11GB BLOWER with 4,352 CUDA cores, 12GB RAM, and 7GBPS memory clock. |
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| # 5 Comparative Analysis |
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| # 5.1 Correlation between Rare Tokens and Creative Essay Evaluation Results |
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| In our study, the main goal is to see if there is any correlation between rare tokens and creative essay evaluation results. Specifically, we present five rare token extraction methods $F \in \{f_1, f_2, f_3, f_4, f_5\}$ . $f_1$ finds rare tokens based on BPE; $f_2$ on IDF; $f_3$ on clustering contextualized vectors in the semantic space; $f_4$ on SpanBERT; and $f_5$ on existing dictionaries about rare words (e.g., Stanford Rare Word dictionary). |
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| Figure 3 shows the average accuracies of ramBERT using $F$ . In the figure, the baseline method is the existing BERT model in which random words are masked and predicted in the pre-training step. |
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| Figure 3: Average accuracies of five rare token extraction methods when the top- $30\%$ of rare tokens are masked. |
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| The average accuracy of the baseline model is $74\%$ or so. On the other hand, the average accuracy of using $f_{1}, f_{2}, f_{3}, f_{4}$ , and $f_{5}$ is $79.2\%$ , $82.7\%$ , $84.2\%$ , $77.2\%$ , and $74.4\%$ , respectively. Compared to the baseline model, ramBERT using $f_{1}, f_{2}, f_{3}, f_{4}$ , and $f_{5}$ improved the accuracy by about $7\%$ , $12\%$ , $14\%$ , $4\%$ , and $0.5\%$ , respectively. Surprisingly, in all cases, ramBERT significantly improved the accuracy over BERT. These experimental results indicate that even rare tokens extracted by any method $f_{i} \in F$ have a strong influence on creative essay assessment. |
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| Especially, among the five methods, rare tokens extracted by $f_{3}$ seem to correlate strongly with creativity evaluation results. Please, note that ramBERT using $f_{3}$ improved the accuracy by up to $14\%$ . Unlike subword-based tokenizing $(f_{1})$ , lexical representation $(f_{2})$ , and advanced word embedding $(f_{5})$ approaches, $f_{3}$ is based on clustering contextualized vectors in the semantic space. This is, rare tokens extracted through this semantic representation approach are more useful than those extracted by other extraction methods. This suggests that rare tokens extracted by considering the context of the corpus are the dominant factor in evaluating creative essays than those extracted using superficial methods such as the lexical representation approach. Furthermore, the semantic representation approach is better than the advanced word embedding method such as Context-sensitive Morphological RNN that is limited to extract rare words by affixes and frequencies. |
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| Moreover, our hypothesis in designing $f_{3}$ is that only tokens corresponding to contextualized vectors belonging to the smallest cluster among several |
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| <table><tr><td></td><td>Φ1</td><td>Φ2</td><td>Φ3</td><td>Φ4</td><td>Φ5</td><td>Φ6</td><td>Φ7</td></tr><tr><td>Φ1</td><td>100.0</td><td>70.5</td><td>47.7</td><td>69.8</td><td>43.1</td><td>8.1</td><td>9.8</td></tr><tr><td>Φ2</td><td>61.6</td><td>100.0</td><td>49.9</td><td>87.2</td><td>45.4</td><td>8.4</td><td>9.2</td></tr><tr><td>Φ3</td><td>47.8</td><td>57.2</td><td>100.0</td><td>98.9</td><td>79.1</td><td>8.4</td><td>7.5</td></tr><tr><td>Φ4</td><td>46.9</td><td>67.0</td><td>64.5</td><td>100.0</td><td>53.2</td><td>8.4</td><td>7.7</td></tr><tr><td>Φ5</td><td>22.9</td><td>27.5</td><td>41.9</td><td>42.0</td><td>100.0</td><td>6.5</td><td>3.5</td></tr><tr><td>Φ6</td><td>5.1</td><td>6.0</td><td>5.3</td><td>7.9</td><td>7.9</td><td>100.0</td><td>1.0</td></tr><tr><td>Φ7</td><td>38.6</td><td>41.4</td><td>29.5</td><td>45.2</td><td>26.1</td><td>6.3</td><td>100.0</td></tr></table> |
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| Table 2: ROUGE-1 of rare token sets. |
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| <table><tr><td></td><td>Φ1</td><td>Φ2</td><td>Φ3</td><td>Φ4</td><td>Φ5</td><td>Φ6</td><td>Φ7</td></tr><tr><td>Φ1</td><td>100.0</td><td>46.6</td><td>20.0</td><td>36.6</td><td>0.05</td><td>2.2</td><td>1.0</td></tr><tr><td>Φ2</td><td>40.7</td><td>100.0</td><td>22.0</td><td>67.4</td><td>0.04</td><td>2.4</td><td>1.2</td></tr><tr><td>Φ3</td><td>20.0</td><td>25.3</td><td>100.0</td><td>58.8</td><td>0.09</td><td>2.3</td><td>0.7</td></tr><tr><td>Φ4</td><td>24.6</td><td>51.9</td><td>39.4</td><td>100.0</td><td>0.03</td><td>2.6</td><td>0.9</td></tr><tr><td>Φ5</td><td>0.02</td><td>0.02</td><td>0.04</td><td>0.02</td><td>100.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Φ6</td><td>1.3</td><td>1.6</td><td>1.4</td><td>2.4</td><td>0.0</td><td>100.0</td><td>0.05</td></tr><tr><td>Φ7</td><td>3.9</td><td>5.1</td><td>2.7</td><td>5.2</td><td>0.0</td><td>0.4</td><td>100.0</td></tr></table> |
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| Table 3: ROUGE-2 of rare token sets. |
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| ones are considered to be rare in a corpus. Our experimental results showed that this hypothesis can be used to extract rare tokens that are helpful in evaluating creative essays. Consequently, Eq. 4 has been experimentally shown to be valid. |
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| As shown in Figure 3, we can observe that rare tokens, regardless of their form, such as subwords, words, $n$ -gram tokens, and spans, have a great impact on creativity evaluation results. However, among subwords, words, $n$ -gram tokens ( $n = 2$ in our experiments), and spans, the word-based rare tokens have the greatest impact on creative essay evaluation. The average accuracies of ramBERT with subwords ( $\Phi_1$ ), words ( $\Phi_2 / \Phi_3 / \Phi_4 / \Phi_7$ ), $n$ -gram tokens ( $\Phi_5$ ), and spans ( $\Phi_6$ ) are $79\%$ , $82.7\% / 84.2\% / 83.1\% / 74.3\%$ , $79.6\%$ , and $77.2\%$ , when the top- $30\%$ of rare tokens are masked. Interestingly, the accuracy of ramBERT using $\Phi_4$ , the union set of rare tokens extracted by both the lexical ( $f_2$ ) and semantic representation ( $f_3$ ) approaches, dropped by about $1.1\%$ . This indicates that using the semantic representation approach alone is more effective than combining lexical and semantic approaches. Another interesting point is that using word-based rare tokens improved ramBERT's accuracy rather than those in the form of $n$ -gram tokens and spans. From these experimental results, we make sure that word-based tokens are more effective than other types of rare tokens because they are an important primitive for context understanding. For lack of space, we will discuss experimental results in more detail in Appendix A. |
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| # 5.2 Characteristics of Extracted Rare Tokens |
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| As some examples of rare tokens, $\Phi_1 = \{\# \# \text{agen}\},$ #icult', 'dar'}, $\Phi_{2} = \{\text{determination},\text{quar}-$ |
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| <table><tr><td></td><td>Φ1</td><td>Φ2</td><td>Φ3</td><td>Φ4</td><td>Φ5</td><td>Φ6</td><td>Φ7</td></tr><tr><td>Φ1</td><td>100.0</td><td>70.5</td><td>47.7</td><td>69.7</td><td>41.8</td><td>8.0</td><td>9.8</td></tr><tr><td>Φ2</td><td>61.5</td><td>100.0</td><td>49.9</td><td>87.2</td><td>44.4</td><td>8.4</td><td>9.1</td></tr><tr><td>Φ3</td><td>47.7</td><td>57.2</td><td>100.0</td><td>98.9</td><td>77.9</td><td>8.4</td><td>7.4</td></tr><tr><td>Φ4</td><td>46.8</td><td>67.0</td><td>64.5</td><td>100.0</td><td>52.3</td><td>8.4</td><td>7.7</td></tr><tr><td>Φ5</td><td>22.2</td><td>26.9</td><td>41.3</td><td>41.3</td><td>100.0</td><td>5.3</td><td>3.3</td></tr><tr><td>Φ6</td><td>5.0</td><td>6.0</td><td>5.3</td><td>7.9</td><td>6.4</td><td>100.0</td><td>1.0</td></tr><tr><td>Φ7</td><td>38.6</td><td>41.0</td><td>29.3</td><td>44.9</td><td>25.0</td><td>6.3</td><td>100.0</td></tr></table> |
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| Table 4: ROUGE-L of rare token sets. |
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| <table><tr><td></td><td>Φ1</td><td>Φ2</td><td>Φ3</td><td>Φ4</td><td>Φ5</td><td>Φ6</td><td>Φ7</td></tr><tr><td>Φ1</td><td>100.0</td><td>73.7</td><td>58.3</td><td>52.2</td><td>0.0</td><td>23.6</td><td>12.3</td></tr><tr><td>Φ2</td><td>74.3</td><td>100.0</td><td>57.2</td><td>74.1</td><td>0.0</td><td>29.4</td><td>12.1</td></tr><tr><td>Φ3</td><td>58.3</td><td>56.6</td><td>100.0</td><td>61.2</td><td>0.0</td><td>23.0</td><td>9.9</td></tr><tr><td>Φ4</td><td>57.2</td><td>76.9</td><td>67.0</td><td>100.0</td><td>0.0</td><td>38.9</td><td>10.4</td></tr><tr><td>Φ5</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>100.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Φ6</td><td>26.0</td><td>30.6</td><td>25.3</td><td>38.9</td><td>0.0</td><td>100.0</td><td>6.9</td></tr><tr><td>Φ7</td><td>2.8</td><td>1.8</td><td>2.2</td><td>0.5</td><td>0.0</td><td>0.3</td><td>100.0</td></tr></table> |
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| antine', 'forfeiture'}, $\Phi_3 = \{\# \# \text{ian},\# \# \text{vis},$ presumably'}, $\Phi_4 = \{\# \# \text{ian},\# \text{presumably},$ ##gu'}, $\Phi_5 = \{\# \text{accur\_confidence},\# \text{prefer\_}$ #uck', phen_\#uv'}, and $\Phi_6 = \{\# \text{prepared memorandum found in},\# \text{engage in genuine consultations},\# \text{pesti}.$ lential burning wind called by'}, and $\Phi_7 = \{\# \text{untracked},\# \text{apocalyptic},\# \text{confinement}\}$ . Unlike other tokens, most tokens in $\Phi_7$ seems to be extremely rare tokens across corpora. All rare tokens, regardless of their form, such as subwords, words, $n$ grams, and spans, are tokenized into subwords that are masked in ramBERT. For example, token 'prodigious' extracted by $f_{3}$ is tokenized into three subwords 'pro', '#dig', and '#ious' which are masked for pre-training ramBERT. |
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| Tables $2\sim 5$ show similarity values for pairs of sets. To measure the similarities, we used ROUGE-1/2/L as recall-based measure and BLEU as precision-based measure. Since both ROUGE and BLEU are the unsymmetrical metrics, the results of $\mathrm{sim}(\Phi_i,\Phi_j)$ and $\mathrm{sim}(\Phi_j,\Phi_i)$ are slightly different. In the table, since $\Phi_5$ is a set of $n$ -gram tokens (2-gram in our experiments), where two consecutive tokens are represented as one token, the similarity values are close to zero. Unexpectedly, we observed that rare tokens extracted by $f_{3}$ do not overlap much with those by $f_{2}$ which are similar to those by $f_{1}$ . This means that rare tokens extracted |
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| Table 5: BLEU of rare token sets. |
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| <table><tr><td></td><td>15%</td><td>30%</td><td>50%</td></tr><tr><td>f1</td><td>77.5</td><td>79.2</td><td>80.1</td></tr><tr><td>f2</td><td>80.2</td><td>82.7</td><td>83.5</td></tr><tr><td>f3</td><td>81.4</td><td>84.2</td><td>85.0</td></tr><tr><td>f4</td><td>75.1</td><td>77.2</td><td>77.9</td></tr><tr><td>f5 (T-Rare)</td><td>74.1</td><td>74.4</td><td>75.1</td></tr></table> |
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| Table 6: Accuracies of ramBERT according to different percentage of masked rare tokens. |
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| by the lexical representation approach are quite different from those by the semantic representation approach. |
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| Table 6 summarizes the accuracies of ramBERT according to different percentage of masked rare tokens. As the percentage of masked rare tokens increases, the accuracy of ramBERT improves, and the accuracy of ramBERT converges when the percentage of masked rare tokens is almost $50\%$ . In $f_{5}$ , we used three different dictionaries about rare words: (1) Harvard Rare Word (H-Rare), (2) Cambridge Rare Word (C-Rare), and (3) Contextual Rare Word (T-Rare). When the top $15\% / 30\% / 50\%$ of rare words are masked, the average accuracy of $f_{5}$ using H-Rare, C-Rare, and T-Rare is $74\% / 74\% / 74.1\%$ , $74.3\% / 74\% / 74.4\%$ , and $75\% / 74.2\% / 75.1\%$ , respectively. These results indicate that there is no significant difference in accuracy when using the three dictionaries. |
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| # 6 Conclusion |
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| In this paper, we proposed a method to detect creative essay writings by using a ramBERT (i.e. rare token masking-based BERT). We used seven different rare token sets and pre-trained a BERT after masking with the rare tokens on a large data. Our preliminary experimental results show that rare tokens are highly correlated with the creativity essay scores. Consequently, the ramBERT improved the accuracy up to $14\%$ compared to a regular BERT which is based on random word masking. The performance improvements are also shown in ROGUE and BLUE scores. |
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| # Limitations |
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| We used the ASAP data set to evaluate the performance of the proposed method. Although the dataset is well known and widely used, it has two major limitations. At first, the data size is small. Even with pre-training the model with a decently large data set (e.g., Wikipedia), the interpretation of experimental results are limited by the data size. The second limitation is an inherited bias in the data set. Since the ASAP data set is labeled by human raters, the data set is biased by personal preferences. At last, the proposed approach requires a reasonably large pre-processing to extract all the additional features which hinders a scalability. Additionally, our work is limited to only measure creativity in expression but not in content. |
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| # Acknowledgements |
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| This work was supported in part by the National Research Foundation of Korea (NRF) Grant by Korean Government through the Ministry of Science and ICT (MSIT) under Grant NRF2022R1A2C1011404. |
| |
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| # A Appendix |
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| Table 7 shows one of high-school student's essays in the ASAP dataset. Interestingly, the baseline model classified it to be non-creative. However, no matter which rare token extraction methods are used, the ramBERT model classified it to be creative. Each token in $d_{i}$ is counted as 1 if it is included in the set of tokens generated by each rare token extraction method. |
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| The number of tokens in $d_{i}$ matched to sets $\Phi_1$ , $\Phi_2$ , $\Phi_3$ , $\Phi_6$ , and $\Phi_7$ are 6, 7, 11, 4, and 0, respectively. The tokens matched to $\Phi_1$ are 'contr', 'distract', 'gorgeous', 'wr', '#fi', and 'distract', where the token like 'distract' appears twice in $d_{i}$ . Those matched to $\Phi_2$ are 'controversial', 'distraction', 'exposer', 'unhealthy', 'gorgeous', 'beneficial', and 'tempting'. Those matched to $\Phi_3$ are 'contr', 'concern', 'concern', 'expose', 'gorgeous', 'concern', 'bene', '#fi', '#st', 'tempt', '#itely', and '#uter'. Those matched to $\Phi_6$ are 'controversial issue in my', 'accessing anything', 'serious concern to', and 'tempting'. |
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| In particular, the reason why the number of tokens matched with $\Phi_7$ is 0 that the number of rare words in $\Phi_7$ is as small as 2,034. Moreover, since the rare words in the Harvard dictionary were generated primarily by affixes and frequencies, it is unlikely that such rare words would appear across several domains. The examples of the Harvard dictionary are 'untracked', 'unflagging', 'unprecedented', 'apocalyptic', 'organismal', 'diagonal', 'obtainment', 'discernment', and 'confinement', where the underlined parts of the rare words are affixes. |
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| The tokens that match $\Phi_2$ , such as 'gorgeous', 'beneficial', and 'tempting', appear to be lexically rare tokens in the corpus of essays. Most tokens matched with $\Phi_3$ , such as 'gorgeous', 'bene', '#fi', and 'tempt', are similar to them matched with $\Phi_2$ , but the number of rare tokens is relatively large. This semantic representation method ( $f_3$ ) tends to extract more rare tokens in addition to the rare tokens extracted through the lexical representation method ( $f_2$ ). Therefore, hidden rare tokens that could not be extracted by the existing lexical representation methods can be extracted through the semantic representation method like $f_3$ . |
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| From the experimental results, we can carefully hypothesize that an essay might be creative expressively if there are many rare tokens in it. Since a detailed discussion of this hypothesis is beyond the |
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| scope of this paper, we do not proceed further here. Instead, we will deeply investigate the validity of this hypothesis through additional in-depth studies. In addition, we will attempt to establish a theory for the hypothesis. |
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| Similarly, we observed in our experimental results that there is a correlation between essay scores and creative essays. The essay scores are evaluators' scores for how well writing is written, regardless of creativity. In the ASAP dataset, the essay scores range from 2 to 12 points, and the higher the score, the better the essay. The essay score of the essay shown in Table 7 is 12 points as well. This is because evaluators give proper scores to student essays in terms of grammar, expressiveness, and composition of writing, but in addition to them, if there is novelty in expression or content, the essay tends to be given a higher score. |
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| I think we can all agree that computer usage is a very controversial issue in my opinion. I believe that computers have a negative effect on people. For instance, it's not safe and children can get into all sorts of things on the Internet. Also, people spend too much time in front the computer now a days it's a major distraction and also a negative effect on kids school work. It's now or never do we decide that computers have a negative effect. You decide isn't every parents biggest concern the safety of their children. When on the Internet kids are capable of accessing anything and everything. Sometimes kids don't even look for bad things they just pop up. Would you want your child viewing things that you have no control over. Also, websites like com one of the greatest concerns when it comes to Internet safety. Although you are supposed to be at least to have a most kids lie about their age. Did you know that out of users lie about their age. And it's not always a year old saying they are it could be a year old saying they're. Not only do people lie about their age they lie about who they are. Is this the kind of Internet exposer you want for you children put a stop to this right now. More than of are overweight and unhealthy. This is another negative effect computers have on people. It's a gorgeous day Bright blue skies cotton candy clouds the sun is shining and there's a nice warm breeze Perfect day to go out and get active right Wrong. None people would be inside on the computer instead of going for a walk people would spend hours on Facebook. This is a serious concern to our health. People don't exercise enough as it is and then when you add computers, people will never get active instead of playing video games online people need to be reminded that turning off the computer and playing a fun neighbourhood game of baseball is just as fun and much more beneficial. This is just one step need to take to get a healthier lifestyle. Wouldn't you agree? Did you know that kids that spend more time on computer are more likely to do poorly in school. Surely if nothing else will convince you of the negative effects of a computer, this will than coming home and doing homework more time is spent in front of the computer. As a student, I will admit that the computer is a very tempting distraction and can easily pull a student away from their studies. You can't expect a child to make the right decision and tell their they have to go because they need to study. So you do take action now, or your child will definitely suffer. The time has come to decide. Do you believe computers have a negative effect on people. It's clear that the computer is not safe. Not to mention too much time is spent on the computer instead of being active. Most importantly, computers will negatively affect children's grades. Don't wait another minute. Let's agree and do something about this. |
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| A For every submission: |
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| A1. Did you describe the limitations of your work? 7 |
| A2. Did you discuss any potential risks of your work? |
| A3. Do the abstract and introduction summarize the paper's main claims? |
| A4. Have you used AI writing assistants when working on this paper? Left blank. |
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| B Did you use or create scientific artifacts? |
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| 3 |
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| B1. Did you cite the creators of artifacts you used? 24 |
| B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. |
| B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 3 |
| B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? 34 |
| B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 34 |
| B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 4 |
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| C Did you run computational experiments? |
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| 4 |
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| C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 4 |
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| C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? |
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| 4 |
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| C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? |
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| 5 |
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| C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? |
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| 4 |
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| D Did you use human annotators (e.g., crowdworkers) or research with human participants? |
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| 4 |
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| D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? |
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| 4 |
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| D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? |
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| 4 |
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| D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? |
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| 4 |
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| D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? |
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| D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? |
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