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20172026
most citedAn Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation

51 citations · 147 across the 23 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.CL2020

A Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences

Andrew Merritt, Chenhui Chu, Yuki Arase

Multimodal neural machine translation (NMT) has become an increasingly important area of research over the years because additional modalities, such as image data, can provide more…

cs.CL2020

Lexically Cohesive Neural Machine Translation with Copy Mechanism

Vipul Mishra, Chenhui Chu, Yuki Arase

Lexically cohesive translations preserve consistency in word choices in document-level translation. We employ a copy mechanism into a context-aware neural machine translation model…

cs.CV2020

Constructing a Visual Relationship Authenticity Dataset

Chenhui Chu, Yuto Takebayashi, Mishra Vipul +1

A visual relationship denotes a relationship between two objects in an image, which can be represented as a triplet of (subject; predicate; object). Visual relationship detection i…

cs.CV20208 cited

A Dataset and Baselines for Visual Question Answering on Art

Noa Garcia, Chentao Ye, Zihua Liu +5

Answering questions related to art pieces (paintings) is a difficult task, as it implies the understanding of not only the visual information that is shown in the picture, but also…

cs.CV2020

Knowledge-Based Visual Question Answering in Videos

Noa Garcia, Mayu Otani, Chenhui Chu +1

We propose a novel video understanding task by fusing knowledge-based and video question answering. First, we introduce KnowIT VQA, a video dataset with 24,282 human-generated ques…

cs.CL202023 cited

A Comprehensive Survey of Multilingual Neural Machine Translation

Raj Dabre, Chenhui Chu, Anoop Kunchukuttan

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality…