We present a scalable method to build a high quality instruction followi...
The emergence of generative pre-trained models has facilitated the synth...
Evaluation of natural language generation (NLG) is complex and
multi-dim...
Given a prefix (context), open-ended generation aims to decode texts tha...
Large language models are trained in two stages: (1) unsupervised pretra...
Scaling up language models has led to unprecedented performance gains, b...
Large-scale generative models show an impressive ability to perform a wi...
The design choices in the Transformer attention mechanism, including wea...
One of the most impressive results of recent NLP history is the ability ...
Fine-tuning large pre-trained language models on downstream tasks has be...
Multilingual neural machine translation (MNMT) learns to translate multi...
A central goal of machine learning is to learn robust representations th...
The quadratic computational and memory complexities of the Transformer's...
In this paper, we focus on the unsupervised setting for structure learni...
Neural sequence models can generate highly fluent sentences but recent
s...
Non-autoregressive machine translation (NAT) systems predict a sequence ...
Most sequence-to-sequence (seq2seq) models are autoregressive; they gene...
Despite impressive empirical successes of neural machine translation (NM...
Recent approaches to cross-lingual word embedding have generally been ba...
This paper describes the ARIEL-CMU submissions to the Low Resource Human...
Variational Autoencoder (VAE), a simple and effective deep generative mo...
Much work in Natural Language Processing (NLP) has been for resource-ric...
Semantic parsing is the task of transducing natural language (NL) uttera...
Labeled sequence transduction is a task of transforming one sequence int...
Neural network models have been demonstrated to be capable of achieving
...
Distributed word representations have been demonstrated to be effective ...