Search Results for author: Muthu Kumar Chandrasekaran

Found 10 papers, 5 papers with code

Overview of the First Workshop on Scholarly Document Processing (SDP)

no code implementations EMNLP (sdp) 2020 Muthu Kumar Chandrasekaran, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard

To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event.

Read Top News First: A Document Reordering Approach for Multi-Document News Summarization

1 code implementation Findings (ACL) 2022 Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi

A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document.

Document Summarization News Summarization

The CL-SciSumm Shared Task 2018: Results and Key Insights

1 code implementation2 Sep 2019 Kokil Jaidka, Michihiro Yasunaga, Muthu Kumar Chandrasekaran, Dragomir Radev, Min-Yen Kan

This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain.

Document Summarization Information Retrieval +1

When to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums

1 code implementation26 May 2019 Muthu Kumar Chandrasekaran, Min-Yen Kan

We propose novel attention based models to infer the amount of latent context necessary to predict instructor intervention.

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).

A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation

no code implementations WS 2016 Hong Jin Kang, Tao Chen, Muthu Kumar Chandrasekaran, Min-Yen Kan

Thus we have also applied word embeddings to the novel task of cross-lingual WSD for Chinese and provide a public dataset for further benchmarking.

Translation Word Embeddings +1

Cannot find the paper you are looking for? You can Submit a new open access paper.