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Found 21 papers, 2 papers with code
Developing Interactive Tourism Planning: A Dialogue Robot System Powered by a Large Language Model
In recent years, large language models (LLMs) have rapidly proliferated and have been utilized in various tasks, including research in dialogue systems.
Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks? -- Investigation of the Effects of Question Marks on Dialogue Systems
When individuals engage in spoken discourse, various phenomena can be observed that differ from those that are apparent in text-based conversation.
Tourist Guidance Robot Based on HyperCLOVA
This paper describes our system submitted to Dialogue Robot Competition 2022.
Balancing Cost and Quality: An Exploration of Human-in-the-loop Frameworks for Automated Short Answer Scoring
Towards guaranteeing high-quality predictions, we present the first study of exploring the use of human-in-the-loop framework for minimizing the grading cost while guaranteeing the grading quality by allowing a SAS model to share the grading task with a human grader.
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges.
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation
We introduce a new task formulation of SAS that matches the actual usage.
Inject Rubrics into Short Answer Grading System
Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance.
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models.
Ranked #10 on
Grammatical Error Correction
on CoNLL-2014 Shared Task
The AIP-Tohoku System at the BEA-2019 Shared Task
We introduce the AIP-Tohoku grammatical error correction (GEC) system for the BEA-2019 shared task in Track 1 (Restricted Track) and Track 2 (Unrestricted Track) using the same system architecture.
Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring
This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?
This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models.
A POS Tagging Model Adapted to Learner English
Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this.
Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English
Part-of-speech (POS) tagging and chunking have been used in tasks targeting learner English; however, to the best our knowledge, few studies have evaluated their performance and no studies have revealed the causes of POS-tagging/chunking errors in detail.
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems
In grammatical error correction (GEC), automatically evaluating system outputs requires gold-standard references, which must be created manually and thus tend to be both expensive and limited in coverage.
