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Found 22 papers, 13 papers with code
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities.
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities.
Unifying Vision, Text, and Layout for Universal Document Processing
UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation.
MACSum: Controllable Summarization with Mixed Attributes
We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning.
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
However, applying such methods to commonsense reasoning tasks faces two unique challenges, i. e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever.
Task Compass: Scaling Multi-task Pre-training with Task Prefix
Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models.
i-Code: An Integrative and Composable Multimodal Learning Framework
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data
Surprisingly, we found that REtrieving from the traINing datA (REINA) only can lead to significant gains on multiple NLG and NLU tasks.
Leveraging Knowledge in Multilingual Commonsense Reasoning
Then we utilize a diverse of 4 English knowledge sources to provide more comprehensive coverage of knowledge in different formats.
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary.
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
The recent proposed Fusion-in-Decoder (FiD), which is built on top of the pretrained generative model T5, achieves the state-of-the-art performance in the reading module.
Does Knowledge Help General NLU? An Empirical Study
It is often observed in knowledge-centric tasks (e. g., common sense question and answering, relation classification) that the integration of external knowledge such as entity representation into language models can help provide useful information to boost the performance.
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
Multimodal pre-training has propelled great advancement in vision-and-language research.
Cluster-Former: Clustering-based Sparse Transformer for Question Answering
Transformer has become ubiquitous in the deep learning field.
Cross-Thought for Sentence Encoder Pre-training
In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering.
Contrastive Distillation on Intermediate Representations for Language Model Compression
Existing language model compression methods mostly use a simple L2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one.
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding
Transformer has become ubiquitous in the deep learning field.
Ranked #1 on
Open-Domain Question Answering
on SearchQA
Accelerating Real-Time Question Answering via Question Generation
Although deep neural networks have achieved tremendous success for question answering (QA), they are still suffering from heavy computational and energy cost for real product deployment.
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
During inference, the model makes predictions based on the text input in the target language and its translation in the source language.
Ranked #18 on
Zero-Shot Cross-Lingual Transfer
on XTREME
Hierarchical Graph Network for Multi-hop Question Answering
In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering.
Ranked #35 on
Question Answering
on HotpotQA
Stochastic Answer Networks for SQuAD 2.0
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is unanswerable or not.
