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Found 12 papers, 7 papers with code
Studying The Impact Of Document-level Context On Simultaneous Neural Machine Translation
To this end and in this paper and we propose wait-k simultaneous document-level NMT where we keep the context encoder as it is and replace the source sentence encoder and target language decoder with their wait-k equivalents.
Gender Bias in Masked Language Models for Multiple Languages
Unfortunately, it was reported that MLMs also learn discriminative biases regarding attributes such as gender and race.
Neural Combinatory Constituency Parsing
We propose two fast neural combinatory models for constituency parsing: binary and multi-branching.
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer.
Simultaneous Multi-Pivot Neural Machine Translation
Parallel corpora are indispensable for training neural machine translation (NMT) models, and parallel corpora for most language pairs do not exist or are scarce.
Towards a Standardized Dataset on Indonesian Named Entity Recognition
In recent years, named entity recognition (NER) tasks in the Indonesian language have undergone extensive development.
Cross-lingual Transfer Learning for Grammatical Error Correction
Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks.
English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs
We introduce our TMU system that is submitted to The 4th Workshop on Neural Generation and Translation (WNGT2020) to English-to-Japanese (En→Ja) track on Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task.
Towards Multimodal Simultaneous Neural Machine Translation
Simultaneous translation involves translating a sentence before the speaker's utterance is completed in order to realize real-time understanding in multiple languages.
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
We introduce our system that is submitted to the News Commentary task (Japanese{\textless}-{\textgreater}Russian) of the 6th Workshop on Asian Translation.
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking.
Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel Corpus
Large-scale parallel corpora are indispensable to train highly accurate machine translators.
Language Modelling
Low-Resource Neural Machine Translation
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