Search Results for author:
Found 18 papers, 10 papers with code
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
To this end, We build a system called \textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps.
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning
Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have relational reasoning and compositional generalization capabilities.
A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation
To further illustrate the commercial value of our approach, we conduct experiments on three generation tasks in real-world advertisements applications.
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation
Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses.
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search
For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build code-text pairs.
POS-Constrained Parallel Decoding for Non-autoregressive Generation
However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation.
Improving Sign Language Translation with Monolingual Data by Sign Back-Translation
Finally, the synthetic parallel data serves as a strong supplement for the end-to-end training of the encoder-decoder SLT framework.
Ranked #4 on
Sign Language Translation
on CSL-Daily
EL-Attention: Memory Efficient Lossless Attention for Generation
Transformer model with multi-head attention requires caching intermediate results for efficient inference in generation tasks.
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation.
Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing
In this paper, we present a multi-level alignment pretraining method in a unified architecture formulti-lingual semantic parsing.
GLGE: A New General Language Generation Evaluation Benchmark
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).
ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine
We build a dataset from a real-word sponsored search engine and carry out experiments to analyze different generative retrieval models.
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
Ranked #6 on
Question Generation
on SQuAD1.1
(using extra training data)
Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching
Given a sentence pair, we extract the output representations of it from BERT.
