Search Results for author: Diane Litman

Found 60 papers, 5 papers with code

Multi-task Learning in Argument Mining for Persuasive Online Discussions

no code implementations EMNLP (ArgMining) 2021 Nhat Tran, Diane Litman

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.

Argument Mining Language Modelling +1

Exploring Multitask Learning for Low-Resource Abstractive Summarization

1 code implementation Findings (EMNLP) 2021 Ahmed Magooda, Diane Litman, Mohamed Elaraby

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.

Abstractive Text Summarization Extractive Summarization +1

Essay Quality Signals as Weak Supervision for Source-based Essay Scoring

no code implementations EACL (BEA) 2021 Haoran Zhang, Diane Litman

However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model.

Automated Essay Scoring

ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining

1 code implementation14 Sep 2022 Zhexiong Liu, Meiqi Guo, Yue Dai, Diane Litman

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.

ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

1 code implementation4 Sep 2022 Mohamed Elaraby, Diane Litman

A challenging task when generating summaries of legal documents is the ability to address their argumentative nature.

Abstractive Text Summarization Argument Mining +1

ArgRewrite V.2: an Annotated Argumentative Revisions Corpus

no code implementations3 Jun 2022 Omid Kashefi, Tazin Afrin, Meghan Dale, Christopher Olshefski, Amanda Godley, Diane Litman, Rebecca Hwa

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.

Self-Driving Cars

Exploring Multitask Learning for Low-Resource AbstractiveSummarization

no code implementations17 Sep 2021 Ahmed Magooda, Mohamed Elaraby, Diane Litman

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.

Abstractive Text Summarization Extractive Summarization +1

Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization

no code implementations Findings (EMNLP) 2021 Ahmed Magooda, Diane Litman

This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data.

Abstractive Text Summarization Data Augmentation

A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations

no code implementations4 Sep 2021 Mingzhi Yu, Diane Litman, Shuang Ma, Jian Wu

Then we use the model to perform similarity measure in a corpus-based entrainment analysis.

Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing

no code implementations WS 2020 Tazin Afrin, Elaine Wang, Diane Litman, Lindsay C. Matsumura, Richard Correnti

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.

Automated Writing Evaluation

Contextual Argument Component Classification for Class Discussions

no code implementations COLING 2020 Luca Lugini, Diane Litman

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.

Classification Component Classification +2

Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring

no code implementations ACL 2020 Haoran Zhang, Diane Litman

While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision.

Automated Essay Scoring Automated Writing Evaluation

The Discussion Tracker Corpus of Collaborative Argumentation

no code implementations LREC 2020 Christopher Olshefski, Luca Lugini, Ravneet Singh, Diane Litman, Amanda Godley

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.

Argument Mining Multi-Task Learning

Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis

no code implementations9 Feb 2020 Ahmed Magooda, Diane Litman

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.

Abstractive Text Summarization Extractive Summarization

Argument Component Classification for Classroom Discussions

no code implementations WS 2018 Luca Lugini, Diane Litman

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.

Classification Component Classification +1

Annotating Student Talk in Text-based Classroom Discussions

no code implementations WS 2018 Luca Lugini, Diane Litman, Amanda Godley, Christopher Olshefski

Classroom discussions in English Language Arts have a positive effect on students' reading, writing and reasoning skills.

Predicting Specificity in Classroom Discussion

no code implementations WS 2017 Luca Lugini, Diane Litman

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.

Identifying Editor Roles in Argumentative Writing from Student Revision Histories

no code implementations3 Sep 2019 Tazin Afrin, Diane Litman

We present a method for identifying editor roles from students' revision behaviors during argumentative writing.

Identifying Personality Traits Using Overlap Dynamics in Multiparty Dialogue

no code implementations2 Sep 2019 Mingzhi Yu, Emer Gilmartin, Diane Litman

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

no code implementations2 Sep 2019 Mingzhi Yu, Diane Litman, Susannah Paletz

Multi-party linguistic entrainment refers to the phenomenon that speakers tend to speak more similarly during conversation.

Co-Attention Based Neural Network for Source-Dependent Essay Scoring

1 code implementation WS 2018 Haoran Zhang, Diane Litman

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring.

Automated Essay Scoring

Word Embedding for Response-To-Text Assessment of Evidence

no code implementations ACL 2017 Haoran Zhang, Diane Litman

Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality.

Automated Essay Scoring

A Novel ILP Framework for Summarizing Content with High Lexical Variety

no code implementations25 Jul 2018 Wencan Luo, Fei Liu, Zitao Liu, Diane Litman

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events.

Abstractive Text Summarization

Weighting Model Based on Group Dynamics to Measure Convergence in Multi-party Dialogue

no code implementations WS 2018 Zahra Rahimi, Diane Litman

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.

An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses

no code implementations COLING 2016 Wencan Luo, Fei Liu, Diane Litman

Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner.

Text Summarization

Automatic Summarization of Student Course Feedback

no code implementations NAACL 2016 Wencan Luo, Fei Liu, Zitao Liu, Diane Litman

Student course feedback is generated daily in both classrooms and online course discussion forums.

A Joint Identification Approach for Argumentative Writing Revisions

no code implementations28 Feb 2017 Fan Zhang, Diane Litman

This paper proposes an approach that identifies the revision location and the revision type jointly to solve the issue of error propagation.

Classification General Classification

Using Discourse Signals for Robust Instructor Intervention Prediction

1 code implementation3 Dec 2016 Muthu Kumar Chandrasekaran, Carrie Demmans Epp, Min-Yen Kan, Diane Litman

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs).

Inferring Discourse Relations from PDTB-style Discourse Labels for Argumentative Revision Classification

no code implementations COLING 2016 Fan Zhang, Diane Litman, Katherine Forbes Riley

Penn Discourse Treebank (PDTB)-style annotation focuses on labeling local discourse relations between text spans and typically ignores larger discourse contexts.

Classification General Classification

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