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Found 23 papers, 7 papers with code
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages
Multilingual neural machine translation models typically handle one source language at a time.
Not All Metrics Are Guilty: Improving NLG Evaluation with LLM Paraphrasing
Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements.
One-stop Training of Multiple Capacity Models
Notably, the analysis demonstrates that our method significantly influences the initial training process, leading to more efficient convergence and superior solutions.
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting
Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages.
Chain-of-Dictionary Prompting Elicits Translation in Large Language Models
Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data.
HanoiT: Enhancing Context-aware Translation via Selective Context
Context-aware neural machine translation aims to use the document-level context to improve translation quality.
GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator
Inspired by the idea of Generative Adversarial Networks (GANs), we propose a GAN-style model for encoder-decoder pre-training by introducing an auxiliary discriminator, unifying the ability of language understanding and generation in a single model.
Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models
Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this paper, we use `bilingual corpora' to denote parallel corpora with `bilingual translation pairs' in many different language pairs, each consisting of two sentences/documents with the same meaning written in different languages.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+3
LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation
To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages.
Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation
Despite the fact that multilingual agreement (MA) has shown its importance for multilingual neural machine translation (MNMT), current methodologies in the field have two shortages: (i) require parallel data between multiple language pairs, which is not always realistic and (ii) optimize the agreement in an ambiguous direction, which hampers the translation performance.
GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation
Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation.
NÜWA-LIP: Language Guided Image Inpainting with Defect-free VQGAN
Language guided image inpainting aims to fill in the defective regions of an image under the guidance of text while keeping non-defective regions unchanged.
SMDT: Selective Memory-Augmented Neural Document Translation
Existing document-level neural machine translation (NMT) models have sufficiently explored different context settings to provide guidance for target generation.
Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task
This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation.
Hierarchical Context-aware Network for Dense Video Event Captioning
Dense video event captioning aims to generate a sequence of descriptive captions for each event in a long untrimmed video.
Multilingual Agreement for Multilingual Neural Machine Translation
Although multilingual neural machine translation (MNMT) enables multiple language translations, the training process is based on independent multilingual objectives.
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation
Standard automatic metrics, e. g. BLEU, are not reliable for document-level MT evaluation.
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders
Multilingual machine translation enables a single model to translate between different languages.
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training.
XGPT: Cross-modal Generative Pre-Training for Image Captioning
While many BERT-based cross-modal pre-trained models produce excellent results on downstream understanding tasks like image-text retrieval and VQA, they cannot be applied to generation tasks directly.
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks.
Ranked #1 on
Action Segmentation
on COIN
(using extra training data)
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior
Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples.
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
On XNLI, 1. 8% averaged accuracy improvement (on 15 languages) is obtained.
Cross-Lingual Natural Language Inference
Cross-Lingual Question Answering
+1
