Discourse Mode Identification in Essays

Song, Wei; Wang, Dong; Fu, Rao; Liu, Lizhen; Liu, Ting; Hu, Guoping

doi:10.18653/v1/p17-1011

Cited by 12 publications

(13 citation statements)

References 38 publications

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“…The aim of designing such systems is to reduce the involvement of human graders as far as possible. AES is a challenging task as it relies on grammar as well as semantics, pragmatics and discourse (Song et al, 2017). Although traditional AES methods typically rely on handcrafted features (Larkey, 1998;Foltz et al, 1999;Attali and Burstein, 2006;Dikli, 2006;Wang and Brown, 2008;Chen and He, 2013;Somasundaran et al, 2014;Yannakoudakis et al, 2014;Phandi et al, 2015), recent results indicate that state-of-the-art deep learning methods reach better performance (Alikaniotis et al, 2016;Dong and Zhang, 2016;Taghipour and Ng, 2016;Song et al, 2017;Tay et al, 2018), perhaps because these methods are able to capture subtle and complex information that is relevant to the task (Dong and Zhang, 2016).…”

Section: Introductionmentioning

confidence: 99%

Automated essay scoring with string kernels and word embeddings

Cozma¹,

Butnaru²,

Ionescu³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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In this work, we present an approach based on combining string kernels and word embeddings for automatic essay scoring. String kernels capture the similarity among strings based on counting common character ngrams, which are a low-level yet powerful type of feature, demonstrating state-of-theart results in various text classification tasks such as Arabic dialect identification or native language identification. To our best knowledge, we are the first to apply string kernels to automatically score essays. We are also the first to combine them with a high-level semantic feature representation, namely the bag-of-super-word-embeddings. We report the best performance on the Automated Student Assessment Prize data set, in both indomain and cross-domain settings, surpassing recent state-of-the-art deep learning approaches.

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Section: Introductionmentioning

confidence: 99%

Automated essay scoring with string kernels and word embeddings

Cozma¹,

Butnaru²,

Ionescu³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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“…Discourse modes are categorized into narration, description, exposition, argument and emotion, following (Song et al, 2017). Moreover, we further identify fine-grained description types, such as appearance, facial expression, action, natural scene, psychology, dialogue and so on.…”

Section: Discourse Mode Recognition For Narrative Essaysmentioning

confidence: 99%

IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation

Gong¹,

Hu²,

Song³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Automated Essay Assessment (AEA) aims to judge students' writing proficiency in an automatic way. This paper presents a Chinese AEA system IFlyEssayAssess (IFlyEA), targeting on evaluating essays written by native Chinese students from primary and junior schools. IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment. It has state-of-the-art grammar level analysis techniques, and also integrates components for rhetoric and discourse level analysis, which are important for evaluating native speakers' writing ability, but still challenging and less studied in previous work. Based on the comprehensive analysis, IFlyEA provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. These services can benefit both teachers and students during the process of writing teaching and learning.

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“…AES is commonly viewed as a supervised learning problem with various feature templates (Larkey, 1998;Attali and Burstein, 2006;Chen and He, 2013;Phandi et al, 2015;Cummins et al, 2016;Song et al, 2017). These methods assume that essay quality correlates with surface-level features.…”

Section: Related Workmentioning

confidence: 99%

Multi-Stage Pre-training for Automated Chinese Essay Scoring

Song¹,

Zhang²,

Fu³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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This paper proposes a pre-training based automated Chinese essay scoring method. The method involves three components: weakly supervised pre-training, supervised crossprompt fine-tuning and supervised targetprompt fine-tuning. An essay scorer is first pretrained on a large essay dataset covering diverse topics and with coarse ratings, i.e., good and poor, which are used as a kind of weak supervision. The pre-trained essay scorer would be further fine-tuned on previously rated essays from existing prompts, which have the same score range with the target prompt and provide extra supervision. At last, the scorer is fine-tuned on the target-prompt training data. The evaluation on four prompts shows that this method can improve a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations.

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Discourse Mode Identification in Essays

Cited by 12 publications

References 38 publications

Automated essay scoring with string kernels and word embeddings

Automated essay scoring with string kernels and word embeddings

IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation

Multi-Stage Pre-training for Automated Chinese Essay Scoring

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