13 citations · 27 across the 15 of their papers we have counts for
16 papers
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan +32
Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey…
IteRABRe: Iterative Recovery-Aided Block Reduction
Haryo Akbarianto Wibowo, Haiyue Song, Hideki Tanaka +3
Large Language Models (LLMs) have grown increasingly expensive to deploy, driving the need for effective model compression techniques. While block pruning offers a straightforward…
Pralekha: Cross-Lingual Document Alignment for Indic Languages
Sanjay Suryanarayanan, Haiyue Song, Mohammed Safi Ur Rahman Khan +2
Mining parallel document pairs for document-level machine translation (MT) remains challenging due to the limitations of existing Cross-Lingual Document Alignment (CLDA) techniques…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…
Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts
Haiyue Song, Raj Dabre, Chenhui Chu +2
Lecture transcript translation helps learners understand online courses, however, building a high-quality lecture machine translation system lacks publicly available parallel corpo…
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation
Haiyue Song, Raj Dabre, Chenhui Chu +2
Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT). Existing work has shown that neural sub-word segmenters are better than Byte-Pair En…
Variable-length Neural Interlingua Representations for Zero-shot Neural Machine Translation
Zhuoyuan Mao, Haiyue Song, Raj Dabre +2
The language-independency of encoded representations within multilingual neural machine translation (MNMT) models is crucial for their generalization ability on zero-shot translati…
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation
Zhuoyuan Mao, Raj Dabre, Qianying Liu +3
This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST). Recent efforts for ZST often utilize the Transformer architecture as the backbone,…
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
Zhen Wan, Fei Cheng, Zhuoyuan Mao +4
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.…
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Zhen Wan, Fei Cheng, Qianying Liu +3
Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks. However, the existing methods ignore the intri…
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
Zhuoyuan Mao, Chenhui Chu, Raj Dabre +3
Word alignment has proven to benefit many-to-many neural machine translation (NMT). However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous…
A System for Worldwide COVID-19 Information Aggregation
Akiko Aizawa, Frederic Bergeron, Junjie Chen +26
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. Meanwhil…
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
Zhuoyuan Mao, Fabien Cromieres, Raj Dabre +2
Neural machine translation (NMT) needs large parallel corpora for state-of-the-art translation quality. Low-resource NMT is typically addressed by transfer learning which leverages…
Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
Haiyue Song, Raj Dabre, Zhuoyuan Mao +3
Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks in low-resource settings. However, large monolingual…
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
Haiyue Song, Raj Dabre, Atsushi Fujita +1
Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a language in…
Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators
Haiyue Song, Chengwen Xu, Qiang Xu +4
Neural approximate computing gains enormous energy-efficiency at the cost of tolerable quality-loss. A neural approximator can map the input data to output while a classifier deter…