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Found 17 papers, 8 papers with code
Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020
This paper describes the Japanese-Chinese Neural Machine Translation (NMT) system submitted by the joint team of Kyoto University and East China Normal University (Kyoto-U+ECNU) to WAT 2020 (Nakazawa et al., 2020).
Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages
This article introduces contrastive alignment instructions (AlignInstruct) to address two challenges in machine translation (MT) on large language models (LLMs).
Variable-length Neural Interlingua Representations for Zero-shot Neural Machine Translation
The language-independency of encoded representations within multilingual neural machine translation (MNMT) models is crucial for their generalization ability on zero-shot translation.
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation
This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST).
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
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. g., fine-tuned BERT) in relation extraction (RE).
LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation
Large-scale language-agnostic sentence embedding models such as LaBSE (Feng et al., 2022) obtain state-of-the-art performance for parallel sentence alignment.
Textual Enhanced Contrastive Learning for Solving Math Word Problems
Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information.
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models.
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems
To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions.
EMS: Efficient and Effective Massively Multilingual Sentence Representation Learning
Massively multilingual sentence representation models, e. g., LASER, SBERT-distill, and LaBSE, help significantly improve cross-lingual downstream tasks.
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks.
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT.
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation
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 language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English.
Lightweight Cross-Lingual Sentence Representation Learning
Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks.
Pre-training via Leveraging Assisting Languages for Neural Machine Translation
Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks.
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
Monolingual pre-training approaches such as MASS (MAsked Sequence to Sequence) are extremely effective in boosting NMT quality for languages with small parallel corpora.
Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
To this end, we propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the LOI.
