Unsupervised MT struggles with morphologically rich languages.
problem Limitations of unsupervised machine translation on morphologically rich languages.
method Adversarial unsupervised alignment of word embedding spaces for bilingual dictionary induction.
result A simple trick exploiting weak supervision from identical words improves unsupervised bilingual dictionary induction performance.
Paper proposes a method to learn word translations bidirectionally.
problem Word translation between languages.
method Jointly learns translations in both directions with minimal supervision.
result Improves accuracy of translations over previous methods.
This research tackles automatic paraphrasing without translation.
problem Automatic paraphrasing without translation.
method Proposes a residual variant of vector-quantized variational auto-encoder trained on an unlabeled monolingual corpus.
result Monolingual paraphrasing outperforms unsupervised translation in all settings.
The authors advocate for more rigorous unsupervised cross-lingual learning methods.
problem Lack of parallel data for many languages.
method Review of unsupervised cross-lingual learning approaches and methodological issues.
result A scenario without parallel data and abundant monolingual data is unrealistic.
City2City translates place representations across cities using language translation techniques.
problem Lack of inter-city perspective in place representations from mobility data.
method Applied unsupervised machine language translation methods to translate place representations across different cities.
result Successfully translated place representations from one city to another, validated using landuse data.
The paper shows how integrating categorical semantics can enhance unsupervised domain translation.
problem Improving unsupervised domain translation between perceptually different domains.
method Learning invariant categorical semantic features in an unsupervised manner and conditioning them on the style encoder.
result Conditioning the style encoder on learned categorical semantics improves translation and stylization.
Novel OT framework for unsupervised domain translation.
problem Finding meaningful correspondence between two unpaired domains.
method Reformulate UDT in Optimal Transport framework, propose novel dynamical framework for efficient computation.
result Existence and uniqueness of optimal mappings proved for a large family of transport costs.
New unsupervised image translation method detects changes without labeled data.
problem Detecting changes in images without labeled data.
method Affinity-based change priors and weighted loss functions trained on convolutional neural networks.
result Proposed method outperforms state-of-the-art algorithms in detecting changes.
Few-shot unsupervised image-to-image translation model learns from a few examples.
problem Current unsupervised image-to-image translation methods require many images at training time.
method Coupling adversarial training with a novel network design for few-shot learning.
result Model achieves effective few-shot image-to-image translation.
This research improves semi-supervised translation using MMD networks.
problem Improving translation quality with limited labeled data.
method Introduces MMD-based unsupervised cost function and n-channel networks.
result Enhanced performance in semi-supervised translation.
Improved unsupervised word translation using adversarial autoencoder with cycle consistency and input reconstruction.
problem Challenging language pairs and lack of parallel data for unsupervised word translation.
method Adversarial autoencoder with cycle consistency and input reconstruction regularization.
result More stable and better performance than recent approaches.
New method transfers between different modalities using latent spaces.
problem Cross-modal domain transfer between pretrained generative models.
method Learning a shared latent space between two generative models.
result Variational autoencoder best bridges latent spaces of different models.
Framework translates images between domains without supervision.
problem Challenges in unsupervised image-to-image translation, especially handling multimodality.
method Proposes a Multimodal Unsupervised Image-to-Image Translation (MUNIT) framework, decomposing images into content and style codes.
result Demonstrates improved generation of diverse outputs from a single source image.
Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the onet…
Universal music translation network across instruments and genres.
problem Translating music across different instruments, genres, and styles.
method Multi-domain wavenet autoencoder with a shared encoder and disentangled latent space trained end-to-end on waveforms.
result Achieves convincing translations even from domains not seen during training.
Federated CycleGAN enables privacy-preserving image translation without central data.
problem Privacy and security issues in unsupervised image-to-image translation.
method Novel federated CycleGAN architecture that decomposes CycleGAN loss into client-specific local objectives.
result Federated CycleGAN achieves comparable performance to non-federated CycleGAN without central data exchange.
Paper develops a new unsupervised scoring function for cross-lingual document alignment.
problem Aligning documents across different languages for NLP tasks.
method Uses cross-lingual sentence embeddings to compute semantic distances and guides document alignment.
result The proposed scoring function outperforms current methods by 7-22% on various language pairs.
L-Cool improves image and language translation by cooling low-density samples.
problem Improving translation performance on fringe samples in unsupervised domain translation.
method Performing Langevin dynamics to move low-density samples towards high-density areas.
result L-Cool enhances state-of-the-art methods in image and language translation tasks.
Enhances image-to-image translation using adversarial latent space.
problem Image-to-image translation task in computer vision.
method Introduces an adversarial discriminator on the latent representation to enforce similar latent space distributions.
result Significantly outperforms competing approaches on MNIST and USPS domain adaptation tasks.
This study examines how the size and alignment of pretraining data affect the performance of large language models on downstream tasks.
problem Understanding how the size and alignment of pretraining data impact the performance of large language models on downstream tasks.
method Investigated the scaling behavior of large language models in a transfer learning setting, focusing on machine translation tasks.
result The size of the finetuning dataset and the distribution alignment between pretraining and downstream data significantly influence the scaling behavior of downstream performance.
Machine learning recreates the periodic table from element properties.
problem Recreating the periodic table using machine learning.
method Unsupervised machine learning with GTM for feature embedding.
result PTG autonomously generates various periodic table layouts.
Proposes using Wasserstein barycenter for better multilingual alignment.
problem Finding word-to-word translations between multiple languages without parallel data.
method Uses Wasserstein barycenter as a more informative pivot language, minimizing pairwise transportation costs.
result Demonstrates state-of-the-art performances on standard benchmarks.
Paper proposes Seq2Seq models for multimodal sentiment analysis.
problem Learning representations from multiple modalities in machine learning.
method Two unsupervised Seq2Seq models for multimodal sentiment analysis.
result Seq2Seq models improve F1 Score by twelve points in Bimodal sentiment analysis.
AR models are unreliable for density estimation, especially for image translation and outlier detection.
problem The reliability of autoregressive models as density estimators in image translation and outlier detection.
method Investigation into AR models' use as density estimators in two settings and examination of underlying optimization issues.
result Density estimates from AR models are unreliable and do not correlate with perceptual quality.
Paper identifies and solves a 'scrambled translation' issue in UNMT models.
problem Scrambled translation in UNMT models using word shuffle noise.
method Retraining UNMT models without noise after a pre-defined number of iterations.
result Retraining strategy improves BLEU scores by 10-20% on various language pairs.
New method uses sequential statistics for classifier parameter estimation without paired data.
problem Removing need for paired input-output data in classification problems.
method Introduces Caesar Cipher analogy and novel loss function for unsupervised learning.
result Estimates classifier parameters using sequential statistics without paired data.
Neural machine translation models trained for 5 South African languages.
problem Lack of resources and research for machine translation in African languages.
method Training neural machine translation models for 5 South African languages using modern techniques.
result Promises of neural machine translation for African languages.
Novel fusion of autoencoders predicts sleepiness from speech.
problem Predicting sleepiness from speech recordings.
method Attention-based and recurrent sequence to sequence autoencoders for unsupervised representation learning.
result Fusion of autoencoders' representations achieves higher correlation with sleepiness scales.
Paper proposes a new unsupervised method for cross-modality data translation without requiring direct mappings.
problem Tackles the challenge of zero-shot cross-modality data translation with fidelity.
method Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion) using score-matching and stochastic diffusion.
result Empirically shows advanced performance compared to other generative models.
The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translat…
Shared task on machine translation using weak user feedback.
problem Learning machine translation from unreliable user feedback.
method Bandit learning approach using real-valued quality estimates.
result Various machine translation architectures and protocols were evaluated.
Tensor2Tensor improves neural machine translation models.
problem Improving neural machine translation models.
method Tensor2Tensor library for deep learning models.
result Tensor2Tensor includes the state-of-the-art Transformer model.
In this paper, we attempt to improve Statistical Machine Translation (SMT) systems on a very diverse set of language pairs (in both directions): Czech - English, Vietnamese - English, French - English and German - English. To accomplish this, we performed translation model training, created adaptations of training sett…
Empirical law predicts accuracy of Google Translate's translation chains.
problem Predicting accuracy in machine translation with multiple hops.
method Empirical testing of Google Translate's sequential translation.
result Accuracy decreases with the number of translating hops, following a power law.
Framework translates unlabeled images between domains.
problem Translating unlabeled images between domains with no supervision.
method Skip-connected encoder-generator structure trained with GAN, cycle, and semantic consistency losses.
result Framework can learn semantic mappings for face images without supervised one-to-one mapping.
An NMT system for Indic languages outperforms Google Translate.
problem Challenges in translating Indic languages efficiently.
method Encoder-decoder with attention mechanism for neural machine translation.
result Outperforms Google Translate with a 6 BLEU score margin on English-Gujarati translation.
Neural machine translation used to convert CUDA to OpenCL.
problem Translating CUDA to OpenCL programs.
method Training input set generation, pre/post processing, case study.
result Improved accuracy in translating CUDA to OpenCL.
New framework for optimal transport with latent global transformations.
problem Optimal transport with latent global transformations in learned representations.
method General framework for optimal transport, joint optimization over couplings and transformations from a flexible class of invariances, algorithms to solve.
result Promising results in various tasks, including unsupervised word translation.
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural ma…
Paper explores using memory models to improve idiomatic language translation.
problem Translation of idiomatic language poses challenges to machine translation systems.
method Uses memory augmented models to enhance neural machine translation of idioms.
result Improves translation of idiomatic language by bridging long-distance dependencies.
Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates…
Deep learning networks learn complex data structures.
problem Understanding the internal functioning of deep learning networks.
method Using complex network analysis techniques to study deep belief networks.
result Gained insights into the structural and functional properties of deep learning networks.
Proposes batch-weight method to correct mode imbalance in domain adaptation.
problem Mode imbalance between source and target distributions affects unsupervised domain transfer.
method Proposes batch-weight method to re-weight training samples.
result Effective in several image-to-image translation tasks.
GNMT uses latent variables to improve machine translation, especially with missing words.
problem Improving machine translation, especially with missing words.
method GNMT adds a latent variable to an encoder-decoder model to capture sentence semantics.
result GNMT achieves competitive BLEU scores and is superior when source sentences are missing words.
Unsupervised learning explained through total correlation.
problem Automating unsupervised learning without clear goals or labels.
method Total Correlation Explanation (CorEx) to learn representations that maximize dependence in data.
result Successes in various domains show that CorEx can effectively learn representations from unlabeled data.
A new neural machine translation method learns from human feedback and reduces human effort.
problem Efficiently reducing human effort in interactive-predictive neural machine translation.
method Learning from human reinforcements, using entropy for feedback triggers, and online model updates.
result Significant improvement in translation quality with reduced human feedback requests.
A new model improves machine translation by accounting for translation ambiguity.
problem Translation ambiguity in parallel corpora leads to inconsistent models.
method A deep generative model with latent variables to handle local variation.
result The model consistently outperforms existing baselines on multiple language pairs.
Study measures gender bias in machine translation using multiple reference points.
problem Measuring and identifying gender bias in machine translation.
method Used an optimal non-biased translator, reference points from occupational statistics and survey.
result Found bias against both genders, but more against women, and found occupations have a greater effect than adjectives.