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Found 19 papers, 2 papers with code
Distinctive Slogan Generation with Reconstruction
To generate a slogan, we apply an encoder–decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization.
Identifying Implicit Quotes for Unsupervised Extractive Summarization of Conversations
The training task of the model is to predict whether a reply candidate is a true reply to a post.
Extractive Summarization
Unsupervised Extractive Summarization
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Existing work commonly relies on fine-tuning weights transferred from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize.
Relation Prediction for Unseen-Entities Using Entity-Word Graphs
Knowledge graphs (KGs) are generally used for various NLP tasks.
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning
The results show that our model generates more consistent headlines, key phrases and categories.
Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification
A simple entity linking approach with text match is used as the document selection component, this component identifies relevant documents for a given claim by using mentioned entities as clues.
Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning
Information extraction about an event can be improved by incorporating external evidence.
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations
We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations.
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts
We proposed a model that integrates discussion structures with neural networks to classify discourse acts.
Using Social Networks to Improve Language Variety Identification with Neural Networks
We propose a hierarchical neural network model for language variety identification that integrates information from a social network.
Character-based Bidirectional LSTM-CRF with words and characters for Japanese Named Entity Recognition
The contributions of this work are (1) verifying the effectiveness of the state-of-the-art NER model for Japanese, (2) proposing a neural model for predicting a tag for each character using word and character information.
Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction
We propose a novel geolocation prediction model using a complex neural network.
Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets
This paper describes our attempt to build a sentiment analysis system for Indonesian tweets.
A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter
In the test run of the task, the model achieved the accuracy of 40. 91{\%} and the median distance error of 69. 50 km in message-level prediction and the accuracy of 47. 55{\%} and the median distance error of 16. 13 km in user-level prediction.
