research
∙
05/29/2023
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
Large-scale pre-trained language models such as GPT-3 have shown remarka...
share
research
∙
08/26/2022
Nearest Neighbor Non-autoregressive Text Generation
Non-autoregressive (NAR) models can generate sentences with less computa...
research
∙
07/27/2022
Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention
Impressive performance of Transformer has been attributed to self-attent...
research
∙
06/01/2022
On Layer Normalizations and Residual Connections in Transformers
In the perspective of a layer normalization (LN) position, the architect...
research
∙
03/25/2022
Single Model Ensemble for Subword Regularized Models in Low-Resource Machine Translation
Subword regularizations use multiple subword segmentations during traini...
research
∙
03/14/2022
Interpretability for Language Learners Using Example-Based Grammatical Error Correction
Grammatical Error Correction (GEC) should not focus only on high accurac...
research
∙
01/14/2022
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
Neural models trained with large amount of parallel data have achieved i...
research
∙
05/26/2021
Joint Optimization of Tokenization and Downstream Model
Since traditional tokenizers are isolated from a downstream task and mod...
research
∙
04/13/2021
Lessons on Parameter Sharing across Layers in Transformers
We propose a parameter sharing method for Transformers (Vaswani et al., ...
research
∙
04/05/2021
Rethinking Perturbations in Encoder-Decoders for Fast Training
We often use perturbations to regularize neural models. For neural encod...
research
∙
10/15/2020
Multi-Task Learning for Cross-Lingual Abstractive Summarization
We present a multi-task learning framework for cross-lingual abstractive...
research
∙
05/02/2020
Improving Truthfulness of Headline Generation
Most studies on abstractive summarization re-port ROUGE scores between s...
research
∙
04/25/2020
All Word Embeddings from One Embedding
In neural network-based models for natural language processing (NLP), th...
research
∙
06/13/2019
Character n-gram Embeddings to Improve RNN Language Models
This paper proposes a novel Recurrent Neural Network (RNN) language mode...
research
∙
04/16/2019
Positional Encoding to Control Output Sequence Length
Neural encoder-decoder models have been successful in natural language g...
research
∙
08/30/2018
Direct Output Connection for a High-Rank Language Model
This paper proposes a state-of-the-art recurrent neural network (RNN) la...
research
∙
12/22/2017
Source-side Prediction for Neural Headline Generation
The encoder-decoder model is widely used in natural language generation ...
research
∙
09/26/2017
Input-to-Output Gate to Improve RNN Language Models
This paper proposes a reinforcing method that refines the output layers ...
research
∙
07/23/2017
