Papers with feature-based approach

5 papers
Automated Essay Scoring System for Nonnative Japanese Learners (2020.lrec-1)

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Challenge: Existing systems only provide a holistic score that summarizes the quality of an essay, which provides little feedback for a language learner.
Approach: They developed an automated essay scoring system for Japanese as a second language learners using an essay dataset with annotations for a holistic score and multiple trait scores.
Outcome: The proposed system achieves the highest accuracy in various natural language processing tasks.
Inferring symmetry in natural language (2020.findings-emnlp)

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Challenge: Empirical work on predicate symmetry has taken two main approaches: feature-based approach and context-based one denies the existence of absolute symmetry.
Approach: They propose a methodological framework for inferring symmetry of verb predicates in natural language.
Outcome: The proposed framework is based on a dataset of 400 naturalistic verbs spanning the spectrum of symmetry-asymmetry.
Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness (2020.coling-main)

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Challenge: lexical semantics theories advocate that the meaning of words is represented as an inference graph, a feature mapping or a cooccurrence vector.
Approach: They propose to probe different lexical semantics theories for their levels of cognitive plausibility and technological usefulness.
Outcome: The proposed approach is superior to the other approaches, the authors argue . they show that the feature-based approach is more useful and more plausible than the other ones .
Conundrums in Cross-Prompt Automated Essay Scoring: Making Sense of the State of the Art (2024.acl-long)

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Challenge: Automated essay scoring (AES) is a task of assigning a single score to an essay . authors abandon sophisticated neural architectures and develop a simple feature-based approach .
Approach: a team of researchers develop a feature-based approach to cross-prompt automated essay scoring that adopts a simple neural architecture.
Outcome: a new approach to cross-prompt automated essay scoring can achieve state-of-the-art results.
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task (2020.lrec-1)

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Challenge: Word embeddings that consider context have attracted great attention for natural language processing tasks in recent years.
Approach: They propose two different approaches to integrate contextualized word embeddings with transformer encoders for sentence similarity modeling.
Outcome: The proposed model outperforms the feature-based approach on six datasets.

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