The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…
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.
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.
This paper uses LLMs and cycle consistency for better machine translation evaluation.
problem Evaluating translation quality and LLM capabilities without ground truth.
method Generate translation candidates, back-translate, and evaluate cycle consistency.
result Larger LLMs or more inference passes improve cycle consistency.
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.
Paper tackles zero-shot translation by encouraging consistent agreement in models.
problem Challenges of generalizing multilingual translation without parallel data.
method Reformulated as probabilistic inference, introduced consistent agreement-based training.
result Agreement-based learning improves zero-shot translation by 2-3 BLEU points.
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.
In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by an NMT model. To achieve this goal, inspired by the recent success …
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 improves NART models for machine translation with faster inference.
problem Inference latency in NART models is high and translation accuracy is inferior.
method Leverages hints from hidden states and word alignments to train NART models.
result Significant improvement in translation accuracy and comparable inference speed to ART baselines.
Paper tackles NAT translation issues with auxiliary regularization.
problem Improves NAT translation quality by addressing repeated and incomplete translations.
method Improves decoder hidden representations via two auxiliary regularization terms.
result Significant improvement in NAT model accuracy with better inference efficiency.
This work improves NAT translation accuracy through fine-tuning with curriculum learning.
problem Improving NAT translation accuracy while maintaining inference speed.
method Curriculum learning applied to fine-tuning of a pre-trained AT model to a NAT model.
result Significant improvement in translation accuracy (over 1 BLEU score) and speedup in inference.
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.
A model learns to translate natural language queries for search systems.
problem Understanding user queries for search-oriented conversational systems.
method Reinforcement learning framework for translating NL expressions to queries.
result Effectiveness of the model on TREC datasets.
Out-of-vocabulary word translation is a major problem for the translation of low-resource languages that suffer from a lack of parallel training data. This paper evaluates the contributions of target-language context models towards the translation of OOV words, specifically in those cases where OOV translations are der…
Model learns diverse molecular transformations for optimization.
problem Optimizing molecules in various ways.
method Junction tree encoder-decoder with adversarial training.
result Model outperforms previous methods on molecular optimization tasks.
We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning…
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…
We propose multi-way, multilingual neural machine translation. The proposed approach enables a single neural translation model to translate between multiple languages, with a number of parameters that grows only linearly with the number of languages. This is made possible by having a single attention mechanism that is …
ConvCNP models translation equivariance in data.
problem Translation equivariance in data.
method Convolutional Conditional Neural Processes (ConvCNP) that embeds data into an infinite-dimensional function space.
result ConvCNP achieves state-of-the-art performance and zero-shot generalization.
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…
UNSB uses neural Schrödinger Bridge to solve unpaired image-to-image translation.
problem Difficulties in unpaired image-to-image translation with diffusion models.
method Expresses SB problem as adversarial learning problems, incorporating advanced discriminators and regularization.
result Successfully solves various unpaired image-to-image translation tasks.
Modeling human language learning with multi-checkpoint machine translation.
problem Improving machine translation quality for language education.
method Ensemble of multi-checkpoints from a single model, sampling n-best sequences.
result Achieved 37.57 macro F1 score, outperforming baseline.
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.
Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-Fr…
A new method for image translation without paired data.
problem Image-to-image translation between two domains with content preservation.
method Energy-based model in latent space of pretrained autoencoder.
result Improved translation quality and content preservation.
BBRT improves molecular properties through iterative translation.
problem Optimizing molecular structures for improved biochemical properties.
method Iterative translation of molecules using a black box approach.
result Improvement in molecular properties with each iteration of the translator.
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 paper proves impossibilities and positive results for universal machine translation.
problem Learning shared sentence representations across multiple language pairs.
method Formal proofs and analysis of natural generative processes.
result Lower bound on translation error and positive results under natural structure.
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 paper presents a neural machine translation system for Wolof.
problem Low-resource conditions for translating French to Wolof.
method Developed a bidirectional LSTM-based encoder-decoder architecture with attention mechanisms.
result Best model achieved a 47% BLEU score for French-Wolof translation.
New translation equivariant neural processes improve spatio-temporal data modeling.
problem Improving posterior prediction maps for spatio-temporal data.
method Introduced translation equivariant transformers within neural processes.
result TE-TNPs outperform non-equivariant TNPs and other baselines.
Improved image translation using asymmetric gradient guidance.
problem Trade-off between style transformation and content preservation in diffusion models.
method Asymmetric gradient guidance to guide reverse diffusion sampling.
result Our method outperforms state-of-the-art models in image translation tasks.
New method learns robust joint representations by translating between modalities.
problem Learning robust joint representations from noisy or missing modalities.
method Cyclic translations between modalities with cycle consistency loss.
result Achieves state-of-the-art results on multimodal sentiment analysis datasets.
Proposes a method to generate diverse translations by conditioning on target domain.
problem NMT models lack diversity in translations, even with search algorithms.
method Condition the decoder on a latent variable representing target domain, generated by a target encoder.
result Generated diverse translations without affecting performance or training time.
We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence. These models lead to better performance, both in terms of general translation quality and pronoun prediction, when trained on small corpora, although this improvement largely disappears when trained…
Trains neural machine translation models for 5 Southern African languages.
problem Lack of shared resources and reproducible results for African languages.
method Trained neural machine translation models on publicly-available datasets.
result Provides code for training and evaluating models, aiming to spur future research.
The task of translating between programming languages differs from the challenge of translating natural languages in that programming languages are designed with a far more rigid set of structural and grammatical rules. Previous work has used a tree-to-tree encoder/decoder model to take advantage of the inherent tree s…
The paper extends Menelaus' and Ceva's theorems to translation triangles in various Thurston geometries.
problem Extending classical theorems to non-Euclidean geometries.
method Using projective models of Thurston geometries and defining a ``surface of a translation-like triangle".
result Generalization of Menelaus' and Ceva's theorems to non-constant curvature Thurston geometries.
Study reveals how machine translation models can leak data privacy.
problem Detecting if a specific data sample was used to train machine translation models.
method Defined membership inference problem for sequence-to-sequence models, tested on machine translation models.
result Machine translation models can leak private information about training data.
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.
NP2MT uses phrase attention to translate phrases efficiently.
problem Phrase-based machine translation inefficiency and performance degradation across different distributions.
method Phrase attention mechanism and dynamic programming for efficient decoding.
result NP2MT achieves comparable performance and outperforms state-of-the-art methods in certain conditions. Paper proposes AXE loss for non-autoregressive machine translation, improving performance.
problem Challenges in training non-autoregressive models due to lack of autoregressive factors and cross entropy loss penalties.
method Proposes aligned cross entropy (AXE) loss function using a differentiable dynamic program for better word order alignment.
result AXE-based training improves performance on major WMT benchmarks and sets a new state of the art for non-autoregressive models.
We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform u…
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.
Paper proposes a new neural machine translation method for wave data.
problem Limited real-world sensor data for continuous signal waves.
method Introduces window-based representation and iterative back-translation for wave data.
result Significant performance improvements in wave translation tasks.
Parallel decoding improves machine translation efficiency and accuracy.
problem Efficiently generating translations from left to right.
method Conditional masked language modeling for parallel decoding.
result Improves translation performance by over 4 BLEU points.
TransCF improves recommendation by modeling user-item relationships with translation vectors.
problem Triangle inequality violation in matrix factorization-based recommendation methods.
method TransCF uses translation vectors to model latent user-item relationships in implicit feedback.
result TransCF outperforms state-of-the-art methods by up to 17% in hit ratio.