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Found 72 papers, 12 papers with code
Inferring Discourse Relations from PDTB-style Discourse Labels for Argumentative Revision Classification
Penn Discourse Treebank (PDTB)-style annotation focuses on labeling local discourse relations between text spans and typically ignores larger discourse contexts.
Using Discourse Signals for Robust Instructor Intervention Prediction
We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs).
A Joint Identification Approach for Argumentative Writing Revisions
This paper proposes an approach that identifies the revision location and the revision type jointly to solve the issue of error propagation.
A Corpus of Annotated Revisions for Studying Argumentative Writing
This paper presents ArgRewrite, a corpus of between-draft revisions of argumentative essays.
Automatic Summarization of Student Course Feedback
Student course feedback is generated daily in both classrooms and online course discussion forums.
An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses
Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner.
Weighting Model Based on Group Dynamics to Measure Convergence in Multi-party Dialogue
This paper proposes a new weighting method for extending a dyad-level measure of convergence to multi-party dialogues by considering group dynamics instead of simply averaging.
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events.
eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
Writing a good essay typically involves students revising an initial paper draft after receiving feedback.
Word Embedding for Response-To-Text Assessment of Evidence
Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality.
Co-Attention Based Neural Network for Source-Dependent Essay Scoring
This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring.
Identifying Personality Traits Using Overlap Dynamics in Multiparty Dialogue
Research on human spoken language has shown that speech plays an important role in identifying speaker personality traits.
Investigating the Relationship between Multi-Party Linguistic Entrainment, Team Characteristics, and the Perception of Team Social Outcomes
Multi-party linguistic entrainment refers to the phenomenon that speakers tend to speak more similarly during conversation.
Identifying Editor Roles in Argumentative Writing from Student Revision Histories
We present a method for identifying editor roles from students' revision behaviors during argumentative writing.
Predicting Specificity in Classroom Discussion
High quality classroom discussion is important to student development, enhancing abilities to express claims, reason about other students' claims, and retain information for longer periods of time.
Annotation and Classification of Sentence-level Revision Improvement
Studies of writing revisions rarely focus on revision quality.
Annotating Student Talk in Text-based Classroom Discussions
Classroom discussions in English Language Arts have a positive effect on students' reading, writing and reasoning skills.
Argument Component Classification for Classroom Discussions
This paper focuses on argument component classification for transcribed spoken classroom discussions, with the goal of automatically classifying student utterances into claims, evidence, and warrants.
Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis
Evaluations demonstrated that summaries produced by the tuned model achieved higher ROUGE scores compared to model trained on just student reflection data or just newspaper data.
The Discussion Tracker Corpus of Collaborative Argumentation
Although Natural Language Processing (NLP) research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals.
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring
While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision.
Contextual Argument Component Classification for Class Discussions
Argument mining systems often consider contextual information, i. e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction.
Discussion Tracker: Supporting Teacher Learning about Students' Collaborative Argumentation in High School Classrooms
Teaching collaborative argumentation is an advanced skill that many K-12 teachers struggle to develop.
Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT
Retrieval-based dialogue systems select the best response from many candidates.
Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing
Automated writing evaluation systems can improve students' writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback.
A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations
Then we use the model to perform similarity measure in a corpus-based entrainment analysis.
Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization
This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data.
Exploring Multitask Learning for Low-Resource AbstractiveSummarization
In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning.
ArgRewrite V.2: an Annotated Argumentative Revisions Corpus
The variety of revision unit scope and purpose granularity levels in ArgRewrite, along with the inclusion of new types of meta-data, can make it a useful resource for research and applications that involve revision analysis.
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining
A challenging task when generating summaries of legal documents is the ability to address their argumentative nature.
ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining
The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e. g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective.
Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions
Speakers build rapport in the process of aligning conversational behaviors with each other.
Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions
Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure.
Extractive Summarization
Unsupervised Extractive Summarization
Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing
We develop models to classify desirable evidence and desirable reasoning revisions in student argumentative writing.
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
We propose a simple approach for the abstractive summarization of long legal opinions that considers the argument structure of the document.
Predicting the Quality of Revisions in Argumentative Writing
The ability to revise in response to feedback is critical to students' writing success.
Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport
While speech-enabled teachable agents have some advantages over typing-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion
Rigorous and interactive class discussions that support students to engage in high-level thinking and reasoning are essential to learning and are a central component of most teaching interventions.
Learning from Auxiliary Sources in Argumentative Revision Classification
We develop models to classify desirable reasoning revisions in argumentative writing.
STRONG -- Structure Controllable Legal Opinion Summary Generation
We propose an approach for the structure controllable summarization of long legal opinions that considers the argument structure of the document.
Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining
This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023.
ReflectSumm: A Benchmark for Course Reflection Summarization
This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students' reflective writing.
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
Exploring Multitask Learning for Low-Resource Abstractive Summarization
In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning.
Multi-task Learning in Argument Mining for Persuasive Online Discussions
We utilize multi-task learning to improve argument mining in persuasive online discussions, in which both micro-level and macro-level argumentation must be taken into consideration.
Self-trained Pretrained Language Models for Evidence Detection
We provide a thorough investigation on how to utilize pseudo labels effectively in the self-training scheme.
Getting Better Dialogue Context for Knowledge Identification by Leveraging Document-level Topic Shift
To build a goal-oriented dialogue system that can generate responses given a knowledge base, identifying the relevant pieces of information to be grounded in is vital.
Essay Quality Signals as Weak Supervision for Source-based Essay Scoring
However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model.
