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Found 14 papers, 8 papers with code
Knowledge-Augmented Language Model Verification
Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters.
Face-StyleSpeech: Improved Face-to-Voice latent mapping for Natural Zero-shot Speech Synthesis from a Face Image
The prosody encoder is specifically designed to model prosodic features that are not captured only with a face image, allowing the face encoder to focus solely on capturing the speaker identity from the face image.
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG.
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
Large Language Models (LLMs) have shown promising performance in knowledge-intensive reasoning tasks that require a compound understanding of knowledge.
ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models
Emotional Text-To-Speech (TTS) is an important task in the development of systems (e. g., human-like dialogue agents) that require natural and emotional speech.
Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models
There has been a significant progress in Text-To-Speech (TTS) synthesis technology in recent years, thanks to the advancement in neural generative modeling.
Self-Distillation for Further Pre-training of Transformers
Pre-training a large transformer model on a massive amount of unlabeled data and fine-tuning it on labeled datasets for diverse downstream tasks has proven to be a successful strategy, for a variety of vision and natural language processing tasks.
KALA: Knowledge-Augmented Language Model Adaptation
Pre-trained language models (PLMs) have achieved remarkable success on various natural language understanding tasks.
Edge Representation Learning with Hypergraphs
This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges.
Learning to Perturb Word Embeddings for Out-of-distribution QA
QA models based on pretrained language mod-els have achieved remarkable performance on various benchmark datasets. However, QA models do not generalize well to unseen data that falls outside the training distribution, due to distributional shifts. Data augmentation (DA) techniques which drop/replace words have shown to be effective in regularizing the model from overfitting to the training data. Yet, they may adversely affect the QA tasks since they incur semantic changes that may lead to wrong answers for the QA task.
Accurate Learning of Graph Representations with Graph Multiset Pooling
Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks.
Ranked #1 on
Graph Classification
on ToxCast
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation
We propose a method to automatically generate a domain- and task-adaptive maskings of the given text for self-supervised pre-training, such that we can effectively adapt the language model to a particular target task (e. g. question answering).
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data
We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, which is difficult to solve with existing QA methods due to their lack of scalability.
Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data
We tackle this problem by proposing a memory network fit for long-term lifelong learning scenario, which we refer to as Long-term Episodic Memory Networks (LEMN), that features a RNN-based retention agent that learns to replace less important memory entries based on the retention probability generated on each entry that is learned to identify data instances of generic importance relative to other memory entries, as well as its historical importance.
