1 |
Enhancing Cross-lingual Prompting with Mask Token Augmentation ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Multilingual AMR Parsing with Noisy Knowledge Distillation ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
A knowledge regularized hierarchical approach for emotion cause analysis
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Neural Rating Regression with Abstractive Tips Generation for Recommendation ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Reader-Aware Multi-Document Summarization via Sparse Coding ...
|
|
|
|
Abstract:
We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RA-MDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the ... : 7 pages, 2 figures, accepted as a full paper at IJCAI 2015 ...
|
|
Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.1504.07324 https://arxiv.org/abs/1504.07324
|
|
BASE
|
|
Hide details
|
|
20 |
Abstractive Multi-Document Summarization via Phrase Selection and Merging ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|