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Found 20 papers, 4 papers with code
Fine-grained Typing of Emerging Entities in Microblogs
Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.
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.
Exploratory Model Analysis Using Data-Driven Neuron Representations
Probing classifiers have been extensively used to inspect whether a model component captures specific linguistic phenomena.
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.
PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts
The meanings of words and phrases depend not only on where they are used (contexts) but also on who use them (writers).
Early Discovery of Disappearing Entities in Microblogs
The major challenge is detecting uncertain contexts of disappearing entities from noisy microblog posts.
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.
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.
uBLEU: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems
Experimental results on massive Twitter data confirmed that υBLEU is comparable to ΔBLEU in terms of its correlation with human judgment and that the state of the art automatic evaluation method, RUBER, is improved by integrating υBLEU.
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.
Early Discovery of Emerging Entities in Microblogs
Keeping up to date on emerging entities that appear every day is indispensable for various applications, such as social-trend analysis and marketing research.
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.
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 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.
A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size
In this paper, we describe the team UT-IIS{'}s system and results for the WAT 2017 translation tasks.
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.
