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Found 28 papers, 13 papers with code
Self-Supervised Quality Estimation for Machine Translation
Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT.
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision
Besides, the robustness of the previous neural methods is limited by the large-scale annotated data.
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance
Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc.
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Modeling Voting for System Combination in Machine Translation
System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Learning to Predict Explainable Plots for Neural Story Generation
While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge.
Neural Machine Translation with Explicit Phrase Alignment
The lack of alignment in NMT models leads to three problems: it is hard to (1) interpret the translation process, (2) impose lexical constraints, and (3) impose structural constraints.
Learning to Copy for Automatic Post-Editing
To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.
Improving Back-Translation with Uncertainty-based Confidence Estimation
While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
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Neural Diffusion Model for Microscopic Cascade Prediction
The strong assumptions oversimplify the complex diffusion mechanism and prevent these models from better fitting real-world cascade data.
Social and Information Networks Physics and Society
Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge.
Improving the Transformer Translation Model with Document-Level Context
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.
Earth Mover's Distance Minimization for Unsupervised Bilingual Lexicon Induction
By viewing word embedding spaces as distributions, we propose to minimize their earth mover{'}s distance, a measure of divergence between distributions.
Visualizing and Understanding Neural Machine Translation
While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks.
Adversarial Training for Unsupervised Bilingual Lexicon Induction
In this work, we show that such cross-lingual connection can actually be established without any form of supervision.
THUMT: An Open Source Toolkit for Neural Machine Translation
This paper introduces THUMT, an open-source toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University.
Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover's Distance Regularization
Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains.
Image-embodied Knowledge Representation Learning
More specifically, we first construct representations for all images of an entity with a neural image encoder.
Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
We introduce an agreement-based approach to learning parallel lexicons and phrases from non-parallel corpora.
Online Collaborative Learning for Open-Vocabulary Visual Classifiers
Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers.
Modeling Relation Paths for Representation Learning of Knowledge Bases
Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space.
