Search Results for author:
Found 8 papers, 3 papers with code
Speculative Sampling in Variational Autoencoders for Dialogue Response Generation
Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response.
Rethinking Response Evaluation from Interlocutor's Eye for Open-Domain Dialogue Systems
Open-domain dialogue systems have started to engage in continuous conversations with humans.
Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation
Prior to fine-tuning, our method replaces the embedding layers of the NMT model by projecting general word embeddings induced from monolingual data in a target domain onto a source-domain embedding space.
Vocabulary Adaptation for Distant Domain Adaptation in Neural Machine Translation
Prior to fine-tuning, our method replaces the embedding layers of the NMT model by projecting general word embeddings induced from monolingual data in a target domain onto a source-domain embedding space.
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings
In this study, we propose a method of modeling such personal biases in word meanings (hereafter, semantic variations) with personalized word embeddings obtained by solving a task on subjective text while regarding words used by different individuals as different words.
Learning to Describe Unknown Phrases with Local and Global Contexts
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities.
Learning to Describe Phrases with Local and Global Contexts
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities.
