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Found 15 papers, 2 papers with code
A Case Study of In-House Competition for Ranking Constructive Comments in a News Service
Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience.
Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
The writing process consists of several stages such as drafting, revising, editing, and proofreading.
Dataset Creation for Ranking Constructive News Comments
Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task.
A Case Study on Neural Headline Generation for Editing Support
There have been many studies on neural headline generation models trained with a lot of (article, headline) pairs.
Frustratingly Easy Model Ensemble for Abstractive Summarization
Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks.
Extractive Headline Generation Based on Learning to Rank for Community Question Answering
User-generated content such as the questions on community question answering (CQA) forums does not always come with appropriate headlines, in contrast to the news articles used in various headline generation tasks.
Pretraining Sentiment Classifiers with Unlabeled Dialog Data
Recent studies showed that pretraining with unlabeled data via a language model can improve the performance of classification models.
Cross-domain Recommendation via Deep Domain Adaptation
The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used.
Incremental Skip-gram Model with Negative Sampling
This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives.
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
We investigate the statistical performance and computational efficiency of the alternating minimization procedure for nonparametric tensor learning.
