51 citations · 181 across the 51 of their papers we have counts for
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Textual Enhanced Contrastive Learning for Solving Math Word Problems
Yibin Shen, Qianying Liu, Zhuoyuan Mao +2
Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies sh…
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction
Zhen Wan, Qianying Liu, Zhuoyuan Mao +3
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations…
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision
Qianying Liu, Wenyu Guan, Jianhao Shen +2
Previous studies have introduced a weakly-supervised paradigm for solving math word problems requiring only the answer value annotation. While these methods search for correct valu…
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems
Yibin Shen, Qianying Liu, Zhuoyuan Mao +3
To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions. However, the mainstream sequence-to-sequence approa…
EMS: Efficient and Effective Massively Multilingual Sentence Embedding Learning
Zhuoyuan Mao, Chenhui Chu, Sadao Kurohashi
Massively multilingual sentence representation models, e.g., LASER, SBERT-distill, and LaBSE, help significantly improve cross-lingual downstream tasks. However, the use of a large…
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…
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Qianying Liu, Zhuo Gong, Zhengdong Yang +8
Low-resource speech recognition has been long-suffering from insufficient training data. In this paper, we propose an approach that leverages neighboring languages to improve low-r…
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation
Zhuoyuan Mao, Chenhui Chu, Sadao Kurohashi
In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for la…
VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation
Yihang Li, Shuichiro Shimizu, Weiqi Gu +2
Existing multimodal machine translation (MMT) datasets consist of images and video captions or general subtitles, which rarely contain linguistic ambiguity, making visual informati…