Search Results for author:
Found 36 papers, 6 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.
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.
Exploratory Model Analysis Using Data-Driven Neuron Representations
Probing classifiers have been extensively used to inspect whether a model component captures specific linguistic phenomena.
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.
Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge
Acquiring factual knowledge for language models (LMs) in low-resource languages poses a serious challenge, thus resorting to cross-lingual transfer in multilingual LMs (ML-LMs).
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.
Summarization-based Data Augmentation for Document Classification
Despite the prevalence of pretrained language models in natural language understanding tasks, understanding lengthy text such as document is still challenging due to the data sparseness problem.
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).
A Unified Generative Approach to Product Attribute-Value Identification
We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text.
Back to Patterns: Efficient Japanese Morphological Analysis with Feature-Sequence Trie
Accurate neural models are much less efficient than non-neural models and are useless for processing billions of social media posts or handling user queries in real time with a limited budget.
Esports Data-to-commentary Generation on Large-scale Data-to-text Dataset
Therefore, in this study, we introduce a task of generating game commentaries from structured data records to address the problem.
Self-Adaptive Named Entity Recognition by Retrieving Unstructured Knowledge
Although named entity recognition (NER) helps us to extract domain-specific entities from text (e. g., artists in the music domain), it is costly to create a large amount of training data or a structured knowledge base to perform accurate NER in the target domain.
Early Discovery of Disappearing Entities in Microblogs
The major challenge is detecting uncertain contexts of disappearing entities from noisy microblog posts.
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction
A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products.
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.
Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model
Although many context-aware neural machine translation models have been proposed to incorporate contexts in translation, most of those models are trained end-to-end on parallel documents aligned in sentence-level.
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.
Data augmentation using back-translation for context-aware neural machine translation
A single sentence does not always convey information that is enough to translate it into other languages.
On the Relation between Position Information and Sentence Length in Neural Machine Translation
Although some approaches such as the attention mechanism have partially remedied the problem, we found that the current standard NMT model, Transformer, has difficulty in translating long sentences compared to the former standard, Recurrent Neural Network (RNN)-based model.
Multilingual Model Using Cross-Task Embedding Projection
We present a method for applying a neural network trained on one (resource-rich) language for a given task to other (resource-poor) languages.
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.
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.
