Informativeness
132 papers with code • 1 benchmarks • 1 datasets
CrisisMMD
Most implemented papers
Learning to Navigate for Fine-grained Classification
yangze0930/NTS-Net
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ECCV 2018
In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher.
NRPA: Neural Recommendation with Personalized Attention
In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
dreasysnail/converse_GAN
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NeurIPS 2018
Responses generated by neural conversational models tend to lack informativeness and diversity.
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
Response Generation by Context-aware Prototype Editing
Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses.
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
andy194673/nlg-sclstm-multiwoz
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EMNLP 2015
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.
Prosody Modifications for Question-Answering in Voice-Only Settings
Many popular form factors of digital assistants---such as Amazon Echo, Apple Homepod, or Google Home---enable the user to hold a conversation with these systems based only on the speech modality.
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.
Pragmatically Informative Text Generation
sIncerass/prag_generation
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NAACL 2019
We improve the informativeness of models for conditional text generation using techniques from computational pragmatics.
Theory and Evaluation Metrics for Learning Disentangled Representations
clarken92/DisentanglementMetrics
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ICLR 2020
We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation.
