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Found 25 papers, 4 papers with code
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning
Parallel texts of Japanese and a non-pro-drop language have the potential of improving the performance of Japanese zero anaphora resolution (ZAR) because pronouns dropped in the former are usually mentioned explicitly in the latter.
Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact
Analyzing the evolution of dialects remains a challenging problem because contact phenomena hinder the application of the standard tree model.
Principal Component Analysis as a Sanity Check for Bayesian Phylolinguistic Reconstruction
Bayesian approaches to reconstructing the evolutionary history of languages rely on the tree model, which assumes that these languages descended from a common ancestor and underwent modifications over time.
Addressing Segmentation Ambiguity in Neural Linguistic Steganography
Previous studies on neural linguistic steganography, except Ueoka et al. (2021), overlook the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper's suspicion.
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model
With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generation-based ones.
Native-like Expression Identification by Contrasting Native and Proficient Second Language Speakers
We propose a novel task of native-like expression identification by contrasting texts written by native speakers and those by proficient second language speakers.
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.
Building a Japanese Typo Dataset from Wikipedia's Revision History
User generated texts contain many typos for which correction is necessary for NLP systems to work.
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives
BERT, a neural network-based language model pre-trained on large corpora, is a breakthrough in natural language processing, significantly outperforming previous state-of-the-art models in numerous tasks.
General Classification
Implicit Discourse Relation Classification
+3
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction
Typical event sequences are an important class of commonsense knowledge.
Minimally Supervised Learning of Affective Events Using Discourse Relations
Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words.
On the Definition of Japanese Word
The annotation guidelines for Universal Dependencies (UD) stipulate that the basic units of dependency annotation are syntactic words, but it is not clear what are syntactic words in Japanese.
Bayesian Learning of Latent Representations of Language Structures
We borrow the concept of representation learning from deep learning research, and we argue that the quest for Greenbergian implicational universals can be reformulated as the learning of good latent representations of languages, or sequences of surface typological features.
Analyzing Correlated Evolution of Multiple Features Using Latent Representations
Statistical phylogenetic models have allowed the quantitative analysis of the evolution of a single categorical feature and a pair of binary features, but correlated evolution involving multiple discrete features is yet to be explored.
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification
Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem.
General Classification
Implicit Discourse Relation Classification
+4
Diachrony-aware Induction of Binary Latent Representations from Typological Features
Although features of linguistic typology are a promising alternative to lexical evidence for tracing evolutionary history of languages, a large number of missing values in the dataset pose serious difficulties for statistical modeling.
Contrasting Vertical and Horizontal Transmission of Typological Features
Linguistic typology provides features that have a potential of uncovering deep phylogenetic relations among the world{'}s languages.
Wikification for Scriptio Continua
To address this problem, we propose to define a separate task that directly links given texts to an external resource, that is, wikification in the case of Wikipedia.
