Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments

By Michael Mohler, Razvan C. Bunescu, Rada Mihalcea. Annual Meeting of the Association for Computational Linguistics, 2011

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SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge (2013, background): “However, for the assessment of responses to short-answer questions (Leacock and Chodorow, 2003; Pulman and Sukkarieh, 2005; Nielsen et al., 2008a; Mohler et al., 2011) and in tutorial dialog systems (Graesser et al., 1999; Glass, 2000; Pon-Barry et al., 2004; Jordan et al., 2006; VanLehn et al.,…”
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity (2017, background): “Examples include Word Sense Disam-biguation (Miller et al., 2012), Information Retrieval (Hliaoutakis et al., 2006), Machine Translation (Lavie and Denkowski, 2009), Lexical Sub-stitution (McCarthy and Navigli, 2009), Question Answering (Mohler et al., 2011), Text Summa-rization (Mohammad and Hirst,”
The Eras and Trends of Automatic Short Answer Grading (2015, methodology): “…use a weighted average of BLEU and LSA that could both be ML features, the latter work of the Mohler '09 system has combined the knowledge-and corpus-based methods into a machine learning system (Mohler et al. 2011), and the UKP-BIU system uses corpusbased features as part of the feature set.”
ASAG2024: A Combined Benchmark for Short Answer Grading (2024, unknown): “Thus, assessing the generalizability of current automated grading methods is challenging.”