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Found 18 papers, 4 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.
Building Large-Scale Japanese Pronunciation-Annotated Corpora for Reading Heteronymous Logograms
In this study, to improve the accuracy of pronunciation prediction, we construct two large-scale Japanese corpora that annotate kanji characters with their pronunciations.
Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images
We conducted experiments on the pathological image dataset we created for this study and showed that the proposed method significantly improves the classification performance compared to existing methods.
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.
Robust Backed-off Estimation of Out-of-Vocabulary Embeddings
In this study, inspired by the processes for creating words from known words, we propose a robust method of estimating oov word embeddings by referring to pre-trained word embeddings for known words with similar surfaces to target oov words.
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
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.
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.
Chunk-based Decoder for Neural Machine Translation
The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk.
Kotonush: Understanding Concepts Based on Values behind Social Media
Kotonush, a system that clarifies people{'}s values on various concepts on the basis of what they write about on social media, is presented.
