SlowGuess/ACL_2020_OCR huggingface dataset:
Automated Evaluation of Writing – 50 Years and Counting
Beata Beigman Klebanov and Nitin Madnani
Educational Testing Service, Princeton, NJ, USA
{bbeigmanklebanov, nmadnani}@ets.org
In this theme paper, we reflect on the progress of Automated Writing Evaluation (AWE), using Ellis Page's seminal 1966 paper to frame the presentation. We discuss some of the current frontiers in the field, and offer some thoughts on the emergent uses of this technology.
1 A Minimal Case for AWE
In a seminal paper on the imminence of automated grading of essays, Page (1966) showed that a high correlation between holistic machine and human scores is possible. He demonstrated automated scoring of 276 essays written by high school students by a system with 32 features, resulting in a multiple  $R = 0.65$  between machine and average human score, after adjustment. He also provided a thoughtful discussion of his ambitions for automated scoring and of the possible objections.
Page made the case that automated evaluation of student writing is needed to take some of the evaluation load off the teachers and to provide students evaluations of their (potentially multiple) drafts with a fast turnaround. He then appealed to the then-burgeoning interest and fascination with machine learning to argue for the feasibility of such an enterprise, namely, that machines can learn how to give the right grades to essays, if trained on an expert-scored sample.
As part of the feasibility argument, Page emphasized the need to carefully define the goal so that success can be judged appropriately. The goal is not a "real" master analysis of the essay the way a human reader would do but merely an imitation that would produce a correlated result (using what Page called proxes - approximations). Page considered this goal to be both useful and achievable.
2 Report Card: Where are We Now?
2.1 Accomplishments
Page's minimal desiderata have certainly been achieved – AWE systems today can score in agreement with the average human rater, at least in some contexts. For example, Pearson's Intelligent Essay Assessor™ (IEA) scores essays written for the Pearson Test of English (PTE) as well as for other contexts: “IEA was developed more than a decade ago and has been used to evaluate millions of essays, from scoring student writing at elementary, secondary and university level, to assessing military leadership skills.” Besides sole automated scoring as for PTE, there are additional contexts where the automated score is used in addition to a human score, such as for essays written for the Graduate Record Examination (GRE®) or for the Test of English as a Foreign Language (TOEFL®). Does this mean that the problem of AWE is solved? Well, not exactly.
2.2 Needs Improvement
Page did anticipate some difficulties for AWE systems. It is instructive to see where we are with those.
2.2.1 Originality
What about the gifted student who is offbeat and original? Won't he be overlooked by the computer? (Page, 1966)
Page's argument is that the original student is not going to be much worse off with a com
<sup>1</sup>It is not our goal to survey in detail techniques that underlie this success. See Ke and Ng (2019) for a recent review.
2https://pearsonpte.com/the-test/ about-our-scores/how-is-the-test-scored/ 3https://www.ets.org/gre/revised_general/ scores/how/
4https://www.ets.org/toefl/ibt/scores/ understand/
7796
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7796-7810 July 5 - 10, 2020. ©2020 Association for Computational Linguistics
puter than with an (average) human reader, because originality is a subjective construct. Thus, once research uncovers objective and measurable aspects of "original" writing, relevant features can be added into an AWE system; finding such aspects, as well as measuring them, is still work in progress. While no current operational scoring system we are aware of is specifically looking for originality, research into aspects of writing that are often considered original is taking place. For example, using data from different tests, Beigman Klebanov and Flor (2013a) and Beigman Klebanov et al. (2018) found that the extent of metaphor use (proportion of metaphorically used words in an essay) correlates with essay quality; Littlemore et al. (2014) likewise found that more skilled writers use metaphor more often. Song et al. (2016) observed a positive correlation between use of parallelism – syntactically similar and semantically related constructors, often used for emphasis or to enhance memorability – in student essays. Some pioneering work has been done on comparing writing that is recognized as outstanding (through receiving prestigious prizes) vs writing that is "merely" good in the domain of scientific journalism (Louis and Nenkova, 2013). Once various indicators of originality can be successfully measured, additional work may be necessary to incorporate these measurements into scoring ecosystems since such indicators may only occur infrequently. One way to achieve this would be to compute a "macro" feature that aggregates multiple such indicators, another would be to direct such essays to a human rater for review.
2.2.2 Gaming
Won't this grading system be easy to con? Can't the shrewd student just put in the proxies which will get a good grade? (Page, 1966)
Certainly, students can and do employ gaming strategies to discover and exploit weaknesses of AWE systems. Such strategies can involve repeating the same paragraphs over and over, varying sentence structure, replacing words with more sophisticated variants, re-using words from the prompt, using general academic words, plagiarizing from other responses or from material found on the Internet, inserting unnecessary shell language - linguistic scaffolding for organizing
claims and arguments, and automated generation of essays (Powers et al., 2001; Bejar et al., 2013, 2014; Higgins and Heilman, 2014; Sobel et al., 2014). Such strategies are generally handled by building in filters or flags for aberrant responses (Higgins et al., 2006; Zhang et al., 2016; Yoon et al., 2018; Cahill et al., 2018). However, developers of AWE systems can never anticipate all possible strategies and may have to react quickly as new ones are discovered in use, by developing new AWE methods to identify them. This cat-and-mouse game is particularly rampant in the context of standardized testing (§3.2). This is one of the reasons standardized tests are often not scored solely by an AWE system but also by a human rater.
2.2.3 Content
We are talking awfully casually about grading subject matter like history. Isn't this a wholly different sort of problem? Aren't we supposed to see that what the students are saying makes sense, above and beyond their using commas in the right places? (Page, 1966)
Indeed, work has been done over the last decade on automated evaluation of written responses for their content and not their general writing quality (Sukkarieh and Bolge, 2008; Mohler et al., 2011; Ziai et al., 2012; Basu et al., 2013; Madnani et al., 2013; Ramachandran et al., 2015; Burrows et al., 2015; Sakaguchi et al., 2015; Madnani et al., 2016; Padó, 2016; Madnani et al., 2017a; Riordan et al., 2017; Kumar et al., 2017; Horbach et al., 2018; Riordan et al., 2019). Scoring for content focuses primarily on what students know, have learned, or can do in a specific subject area such as Computer Science, Biology, or Music, with the fluency of the response being secondary. For example, some spelling or grammar errors are acceptable as long as the desired specific information (e.g., scientific principles, trends in a graph, or details from a reading passage) is included in the response. Note that most current content scoring systems ascertain the "correctness" of a response based on its similarity to other responses that humans have deemed to be correct or, at least, high-scoring; they do not employ explicit fact-checking or reasoning for this purpose.
Concerns about specific content extends to other cases where the scoring system needs to pay
7797
attention to details of genre and task – not all essays are five-paragraph persuasive essays; the specific task might require assessing whether the student has appropriately used specific source materials (Beigman Klebanov et al., 2014; Rahimi et al., 2017; Zhang and Litman, 2018) or assessing narrative (Somasundaran et al., 2018) or reflective (Beigman Klebanov et al., 2016a; Luo and Litman, 2016), rather than persuasive, writing.
2.2.4 Feedback
Page emphasized the importance of feedback, and considered the following to be "the sort of feedback that can almost be programmed right now" (original italics):
John [...] please correct the following misspellings: believe, receive. Note the ie/ei problem. You overuse the words interesting, good, nice; then was repeated six times. Check trite expressions. All of your sentences are of the subject-verb variety and all are declarative. Reconstruct. Check subject-verb agreement in second paragraph. You had trouble with this in your last paper. Title lacking. Do the following related assignments for tomorrow ... (Page, 1966)
Today a substantial amount of writing feedback, particularly about spelling and grammar, is incorporated into widely used text editors such as Microsoft Word, Google Docs, and Overleaf. Dedicated writing assistance software such as ETS's Writing Mentor®5 (Burstein et al., 2018), ASU's Writing Pal⁶ (Roscoe and McNamara, 2013; Allen et al., 2014), ETS' Criterion®7 (Burstein et al., 2004), Grammarly's Writing Assistant, CambridgeEnglish's Write & Improve, Ginger's Essay Checker, TurnItIn's Revision Assistant, Vantage Learning's MY Access!, Pearson's My Writing Lab Writing Practice Module and WriteToLearn™13,14 typically go beyond grammar
5 https://mentormywriting.org/  
6 http://www.adaptiveliteracy.com/writing-pal  
7 http://www.ets.org/criterion  
8 https://www.grammarly.com/  
9 https://writeandimprove.com/  
10 https://www.gingersoftware.com/essay-checker  
11 https://www.turnitin.com/products/  
revision-assistant  
12 http://www.vantagelearning.com/products/  
my-access-school-edition/  
13 https://www.pearsonmylabandmastering.com  
14 http://wtl.pearsonkt.com
and spelling. $^{15}$  Such tools provide feedback on discourse structure (Criterion), topic development and coherence (Writing Mentor), tone (Writing Assistant, Rao and Tetrault (2018)), thesis relevance (Writing Pal), sentence "spicing" through suggestions of synonyms and idioms (Ginger's Sentence Rephraser), and style & argumentation-related feedback (Revision Assistant).
Can we then put a green check-mark against Page's agenda for automated feedback, which "may magnify and disseminate the best human capacities to criticize, evaluate, and correct"? Alas, not yet; research on effectiveness of automated feedback on writing is inconclusive (Englert et al., 2007; Shermis et al., 2008; Grimes and Warschauer, 2010; Choi, 2010; Roscoe and McNamara, 2013; Wilson and Czik, 2016; Wilson, 2017; Bai and Hu, 2017; Ranalli et al., 2017). One potential reason for the different outcomes is difference in user populations – feedback that works for L1 writers might not work for L2 writers; differences in ages, skill levels, presence or absence of learning disabilities could all play a role. Adjustment of the evaluation methodology to the specific purpose of the writing assistance tool is another issue for consideration; we will return to this issue in §4.
3 Going off the Page
So far, Page's outline of the promises and challenges of AWE have provided a good framework for surveying the field. There are also a number of developments that were not mapped on Page's chart; we turn to reviewing those next.
3.1 Assessing writing in multiple languages
In order to advance the work on understanding and assessing writing quality, there is clearly a need for a multi-lingual perspective, since methods developed for one language or dialect may not work for another. This consideration does not appear in Page (1966), yet it is an active line of subsequent work. While most of the research we cited so far has been on English, various aspects of writing evaluation, e.g., annotation, detection of various types of errors, and building AWE systems, have been researched for a variety of languages: Song et al. (2016), Rao et al. (2017), Shiue et al. (2017) worked with data in Chinese,
$^{15}$ Writing Pal does not provide specific grammar and spelling feedback.
Lorenzen et al. (2019) in Danish, Berggren et al. (2019) in Norwegian, Amorim and Veloso (2017) in Portuguese, Stymne et al. (2017) in Swedish, Berkling (2018) and Weiss and Meurers (2019) in German, Mezher and Omar (2016) in Arabic, Kakkonen et al. (2005) in Finnish, Loraksa and Peachavanish (2007) in Thai, Lemaire and Dessus (2001) in French, and Ishioka and Kameda (2006) in Japanese. The list is by no means exhaustive; see Flor and Cahill (2020) for a recent review.
3.2 Standardized Testing
The use of automated evaluation technology envisioned by Page was as a service to reduce a teacher's burden; to eventually "lift from the shoulders of the English teacher, that brave and harried soul, his perpetual pressure of unassigned papers, or his unassuaged guilt." While such use has certainly been made (Burstein et al., 2004; Grimes and Warschauer, 2010), the most visible use case for AWE technology has arguably evolved to be in the context of standardized testing, be it for a test of English such as TOEFL® or PTE, a broader, more advanced psychometric examination such as the GRE® or GMAT, or for professional licensure such as AICPA or PRAXIS®.
This development of often high-stakes usage has led to somewhat different challenges from those that Page had anticipated. These challenges generally fall under the purview of the field of educational measurement (Bennett and Bejar, 1998; Clauser et al., 2002; Williamson et al., 2012): How to ensure that the automatic scores assigned to test takers are (1) valid, i.e., they actually measure the skill that the test developer designed the test to measure, (2) defensible, i.e., there is a reasonably clear explanation of why test takers received the particular scores they did, and (3) fair to all the test takers. We address each of these challenges separately below. Note that an additional challenge of high-stakes usage, not elaborated on here, is how to architect scoring systems for large-scale, low-latency use which requires them to be reliable, scalable, flexible, and attentive to the choice of software and application frameworks (Madnani et al., 2018).
3.2.1 Construct Validity
Page declares that he is not after "generating measures of what the true characteristics of the essays are, as ordinarily discussed by human raters" but rather is content "to settle for the correlates of
these true characteristics." Page seems to do away rather quickly with trying to measure the actual thing – the set of all and only "true characteristics of essays", or trins. Why is that? He explains:
Notwithstanding the wonders of the computer, we have to develop a strategy in order to tell the computer what to do. The difficult part is the development of this strategy. It is difficult because we do not really understand what the psychological components are in the judgment of essays. It is easy enough to get persons to expound authoritatively on such judgment, but the fuzziness and inutility of their thinking becomes at once evident when the effort is made to translate it into a computer program. (Page, 1966)
Page's argument is that we do not know precisely enough what the human raters are doing to try and implement that. Some work on rater cognition has already been done in the early 1950s and 1960s, e.g., in the context of the College Entrance Examination Board's development of the General Composition Test. Diederich et al. (1961) had 53 distinguished individuals from various academic disciplines and beyond (English, Social Science, Natural Science, Law, Writers and Editors, Business Executives) sort student essays "in order of merit", with no definition thereof, instructing readers as follows:
Use your own judgment as to what constitutes "writing ability." Do not assume that we want you to do this or that. We want you to use whatever hunches, intuitions, or preferences you normally use in deciding that one piece of writing is better than another. You need not even act as a representative of your field, since individuals in any field have varying tastes and standards.
Readers were also asked to write brief comments on anything that they liked or disliked about the essay, on as many essays as possible. For the study, a sample of U.S. college freshmen were asked to write essays in response to four topics as part of homework. A total of 300 essays addressing two topics were chosen for the analyses, sampled so as to make sure that the full range of abilities is represented (approximated via SAT Verbal
scores). The researchers performed a factor analysis on the matrix of pairwise correlations among the readers, and identified groups of readers (factors) that represent five "schools of thought" about writing quality. Analyzing the comments made by readers who belong to the different "schools of thought", they identified five categories that were each prioritized by one of the groups of readers:
1. Ideas (including relevance, clarity, quantity, development, persuasiveness)
2. Form (including spelling, organization, analysis, coherence)
3. Flavor (including style, originality, quality of ideas, interest, sincerity)
4. Mechanics (including punctuation, grammar, sentence structure, phrasing)
5. Wording (including felicity of expression, comments on specific word choices, cliches)
It is based on such findings above that general scoring criteria have emerged (Deane, 2013) and morphed into scoring rubrics. These are explicit criteria set by and for human raters for evaluating essays. For example, to score highly on the GRE® Issue essay-writing task,[16] one typically:
- articulates a clear and insightful position on the issue in accordance with the assigned task
- develops the position fully with compelling reasons and/or persuasive examples
- sustains a well-focused, well-organized analysis, connecting ideas logically
- conveys ideas fluently and precisely, using effective vocabulary and sentence variety
- demonstrates superior facility with the conventions of standard written English (i.e., grammar, usage and mechanics), but may have minor errors
In the current practice of automated scoring of standardized tests, developers of a scoring engine often need to provide a construct validity argument in order to show that what the system is measuring is actually aligned with the "writing construct" - the actual set of writing skills that the test is supposed to measure.
Some of the items in a human-oriented scoring rubrics are amenable to reasonably direct implementation, often with the help of human-annotated gold standard data such as misspellings (Flor, 2012; Flor and Futagi, 2013) and specific grammar errors (Rozovskaya and Roth, 2010; Leacock et al., 2014). It might be the case that the system would miss some grammar errors and declare an error where there is none, but a grammar assessment system can be built for identifying specific, observable instances of errors that a human reader focused on. Mechanics would likely pick upon.
For other items in a rubric, one might need to drill down, articulate a reliable guideline for humans to assess that particular aspect of the essay, annotate a substantial enough number of essays using the guidelines to make machine learning possible, and then find automatically measurable properties of essays that would provide information relevant to that particular aspect of essay quality. This would be a mix between what Page called a prox and a trin, in that a particular, intrinsically interesting, aspect of an essay can be identified reliably by humans, and an automated system can learn how to approximate that particular construct. Such approaches have been developed for organization (well-organized) (Burstein et al., 2003), coherence (well-focused, conveys ideas fluently) (Burstein et al., 2010; Somasundaran et al., 2014), grammaticality (facility with conventions) (Heilman et al., 2014), thesis clarity (clarity) (Persing and Ng, 2013) as well as aspects of scoring rubrics that are more task-specific, e.g., argumentation (clear position, with compelling reasons) (Stab and Gurevych, 2014; Ghosh et al., 2016; Beigman Klebanov et al., 2017; Stab and Gurevych, 2017; Carlile et al., 2018), use of evidence in the context of source-based writing (Rahimi et al., 2017).
Finally, for some rubric items, it is not clear exactly how to reliably translate the relevant aspect of the writing construct into annotations guidelines, and so proxies might be employed. For example, consider Page's argument for capturing "diction" (appropriate word choice) through word frequency - a writer who can use many different words, including rarer and often semantically nuanced ones, is likelier to make precise word choices than a writer who uses a more limited vocabulary. Attempts to capture topicality (Beigman Klebanov et al., 2016b) or development
$^{16}$ https://www.ets.org/gre/revised_general/prepare/analytical-writing/issue/scoring_guide
7800
(Beigman Klebanov and Flor, 2013b; Somasundaran et al., 2016) through properties of vocabulary distribution without human annotation of topicality and development exemplify such approaches.
3.2.2 Model Interpretability
Recent research has shown that more sophisticated machine learning models might perform better than simple regression-based models when it comes to predictive accuracy (Chen and He, 2013; Cummins et al., 2016; Taghipour and Ng, 2016; Alikaniotis et al., 2016; Dong et al., 2017; Dasgupta et al., 2018; Jin et al., 2018). However, unlike linear regression where stakeholders can understand how much each feature used in the model contributed to the predicted score, many of the more complex models are essentially "black boxes" and do not really lend themselves to post-hoc interpretability (Lipton, 2016). Although interpretability is an active area of research in the machine learning literature (Ribeiro et al., 2016; Koh and Liang, 2017; Doshi-Velez and Kim, 2017), it currently lags behind the research on machine learning methods. For this reason, some automated scoring systems used for high-stakes standardized testing – like ETS's e-Rater (Attali and Burstein, 2006) – still use some variant of least squares linear regression as the machine learning model to predict test taker scores.
3.3 Increased Attention to Fairness
It would probably not be an overstatement to say that fairness in AI is quickly becoming its own sub-field, with a new annual ACM conference on Fairness, Accountability, and Transparency having been inaugurated in  $2018^{17}$  and relevant research appearing at many impactful publication venues, such as Science (Caliskan et al., 2017), NIPS (Pleiss et al., 2017; Kim et al., 2018), ICML (Kearns et al., 2018), ACL (Hovy and Spruit, 2016; Sun et al., 2019; Sap et al., 2019), KDD (Speicher et al., 2018), AAAI (Zhang and Barenboim, 2018), and others (Dwork et al., 2012; Hajian and Domingo-Ferrer, 2013). There is also recent work that examines fairness and ethical considerations when using AI in an education (Mayfield et al., 2019; Gardner et al., 2019).
In the context of assessment, fairness considerations dictate that the test reflects the same construct(s) for the entire test taking population, that
scores from the test have the same meaning for all the test taking population, and that a fair test does not offer undue advantages (or disadvantages) to some individuals because of their characteristics – such as those associated with race, ethnicity, gender, age, socioeconomic status, or linguistic or cultural background – or the test characteristics itself, e.g., the different prompts shown to different test-takers at test time.