Search Results for author:
Found 25 papers, 1 papers with code
Utterance Position-Aware Dialogue Act Recognition
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position.
Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data
As one of the NER improvement approaches, multi-task learning that learns a model from multiple training data has been used.
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation
Therefore, we create a benchmark test dataset for Japanese-to-English MLCC-MT from the Newsela corpus by introducing an automatic filtering of data with inappropriate sentence-level complexity, manual check for parallel target language sentences with different complexity levels, and manual translation.
Contrastive Response Pairs for Automatic Evaluation of Non-task-oriented Neural Conversational Models
Responses generated by neural conversational models (NCMs) for non-task-oriented systems are difficult to evaluate.
Synchronous Syntactic Attention for Transformer Neural Machine Translation
This paper proposes a novel attention mechanism for Transformer Neural Machine Translation, {``}Synchronous Syntactic Attention,{''} inspired by synchronous dependency grammars.
Hie-BART: Document Summarization with Hierarchical BART
This paper proposes a new abstractive document summarization model, hierarchical BART (Hie-BART), which captures hierarchical structures of a document (i. e., sentence-word structures) in the BART model.
Ranked #7 on
Document Summarization
on CNN / Daily Mail
Bilingual Subword Segmentation for Neural Machine Translation
This paper proposed a new subword segmentation method for neural machine translation, {``}Bilingual Subword Segmentation,{''} which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation.
Supervised Visual Attention for Multimodal Neural Machine Translation
This paper proposed a supervised visual attention mechanism for multimodal neural machine translation (MNMT), trained with constraints based on manual alignments between words in a sentence and their corresponding regions of an image.
Transformer-based Approach for Predicting Chemical Compound Structures
Instead of handcrafted rules, we propose Transformer-based models that predict SMILES strings from chemical compound names.
A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking
To verify our dataset, we performed phrase localization experiments in both languages and investigated the effectiveness of our Japanese annotations as well as multilingual learning realized by our dataset.
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing
We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical com- pound paraphrase model.
Dependency-Based Self-Attention for Transformer NMT
In this paper, we propose a new Transformer neural machine translation (NMT) model that incorporates dependency relations into self-attention on both source and target sides, dependency-based self-attention.
Dependency-Based Relative Positional Encoding for Transformer NMT
This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism.
Improving Neural Machine Translation with Neural Syntactic Distance
The explicit use of syntactic information has been proved useful for neural machine translation (NMT).
Neural Machine Translation Incorporating Named Entity
To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word.
Forest-Based Neural Machine Translation
Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors.
CKY-based Convolutional Attention for Neural Machine Translation
This paper proposes a new attention mechanism for neural machine translation (NMT) based on convolutional neural networks (CNNs), which is inspired by the CKY algorithm.
Neural Machine Translation with Source Dependency Representation
Source dependency information has been successfully introduced into statistical machine translation.
