Search Results for author:
Found 24 papers, 16 papers with code
BjTT: A Large-scale Multimodal Dataset for Traffic Prediction
The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations.
Linear Attention via Orthogonal Memory
Given that orthogonal memory compresses global information, we further dissect the context to amplify fine-grained local information.
Attentive Multi-Layer Perceptron for Non-autoregressive Generation
Furthermore, we marry AMLP with popular NAR models, deriving a highly efficient NAR-AMLP architecture with linear time and space complexity.
DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models
Diffusion models have gained prominence in generating high-quality sequences of text.
Optimizing Non-Autoregressive Transformers with Contrastive Learning
In this paper, we propose to ease the difficulty of modality learning via sampling from the model distribution instead of the data distribution.
OpenICL: An Open-Source Framework for In-context Learning
However, the implementation of ICL is sophisticated due to the diverse retrieval and inference methods involved, as well as the varying pre-processing requirements for different models, datasets, and tasks.
Compositional Exemplars for In-context Learning
The performance of ICL is highly dominated by the quality of the selected in-context examples.
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
To improve the quality of dataset synthesis, we propose a progressive zero-shot dataset generation framework, ProGen, which leverages the feedback from the task-specific model to guide the generation of new training data via in-context examples.
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
Bringing together theoretical analysis and empirical evidence, we demonstrate the great potential of diffusion models in complex conditional language generation tasks.
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
In this paper, we propose Comprehensive Attention Benchmark (CAB) under a fine-grained attention taxonomy with four distinguishable attention patterns, namely, noncausal self, causal self, noncausal cross, and causal cross attentions.
PARAGEN : A Parallel Generation Toolkit
PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation.
CoNT: Contrastive Neural Text Generation
We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation.
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs).
Learning Logic Rules for Document-level Relation Extraction
LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor.
Ranked #21 on
Relation Extraction
on DocRED
The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
This paper describes the Volctrans' submission to the WMT21 news translation shared task for German->English translation.
Counter-Interference Adapter for Multilingual Machine Translation
Developing a unified multilingual model has long been a pursuit for machine translation.
Alleviate Exposure Bias in Sequence Prediction \\ with Recurrent Neural Networks
The key idea is that at each time step, the network takes as input a ``bundle'' of similar words predicted at the previous step instead of a single ground truth.
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information
We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs?
Ranked #3 on
Machine Translation
on WMT2014 English-French
(using extra training data)
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
We propose adversarial uncertainty sampling in discrete space (AUSDS) to retrieve informative unlabeled samples more efficiently.
Non-autoregressive Transformer by Position Learning
Non-autoregressive models are promising on various text generation tasks.
Cross-Lingual Vision-Language Navigation
Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics.
PNAT: Non-autoregressive Transformer by Position Learning
However, position modeling of output words is an essential problem in non-autoregressive text generation.
Neural Phrase-to-Phrase Machine Translation
We also design an efficient dynamic programming algorithm to decode segments that allows the model to be trained faster than the existing neural phrase-based machine translation method by Huang et al. (2018).
