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.
Improves machine translation by learning from user feedback.
problem Avoid showing inferior translations to users.
method Analyze degeneracies of counterfactual learning methods.
result Relates degeneracies to recent counterfactual learning techniques.
A new model CDT improves cross-domain recommendation by learning translation vectors.
problem Improving cross-domain recommendation by transferring user preferences across different platforms.
method Translation-based learning approach with embedding space and factorization machines.
result The proposed CDT model outperforms other cross-domain strategies in six publicly available tasks.
This paper calculates interaction strength for translation surfaces with multiple singularities.
problem Computing the interaction strength of translation surfaces with multiple singularities is challenging.
method The authors study interaction strength of specific families of translation surfaces, including regular polygons and Bouw-Möller surfaces.
result The paper provides exact computations of KVol on translation surfaces with multiple singularities.
Improved learning to reweight using deep interactions between student and teacher models.
problem Limitation of existing learning to reweight methods in utilizing student model's internal states.
method Proposes an algorithm that uses the student model's internal states to the teacher model, which returns adaptive weights to enhance student model training.
result Significant improvement over previous methods in image classification and neural machine translation experiments.
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.
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.
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 optimizes model performance and interpretability for tabular data.
problem Balancing model performance and interpretability in machine learning models.
method Model-agnostic multi-objective optimization framework with evolutionary algorithm.
result Framework generates diverse models that trade off performance and interpretability efficiently.
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.
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…
Proposes local coordinate frames for improving model performance in complex dynamical systems.
problem Improving model performance in complex, non-linear, and time-dependent dynamical systems.
method Introduces roto-translation invariant local coordinate frames for geometric graphs.
result The approach outperforms state-of-the-art models in various complex scenarios.
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.
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.
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.
Improves cross-lingual NER by projecting entities from one language to another.
problem Limited annotated corpora for named entity recognition in many languages.
method Uses machine translation twice: first for sentences, then for entities; matches based on ortho- and phonetic similarity; identifies matches using distributional statistics.
result Improves cross-lingual NER by an average of 4.1 points on 5 diverse languages.
Forward translation improves neural machine translation for sentences originally in source language.
problem Improving neural machine translation quality using synthetic data.
method Case study with French-English news translation, separating test sets by original language, analyzing domains, translationese, and noise.
result Forward translation delivers superior gains on sentences originally in source language, complementing back-translation on target language sentences.
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 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.
The paper introduces a method to assess machine translation quality with confidence intervals.
problem Evaluating the uncertainty and quality of machine translation.
method Utilizes conformal predictive distributions to produce prediction intervals with guaranteed coverage.
result The method outperforms a baseline on six language pairs in terms of coverage and sharpness.
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.
Extracts parallel sentences for machine translation.
problem Data sparsity in multilingual natural language processing.
method Bidirectional recurrent neural network approach.
result Significant improvements in machine translation performance.
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.
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.
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. Self-regulation improves sequence-to-sequence learning by choosing feedback types.
problem Different types of feedback have varying costs and effects on learning.
method Self-regulation strategies decide when to ask for different types of feedback.
result Self-regulator discovers optimal cost-quality trade-off by mixing feedback types.
Memory-augmented neural networks improve machine translation performance.
problem Improving machine translation accuracy and flexibility.
method Evaluation of Neural Turing Machines and Differentiable Neural Computers for machine translation tasks.
result Memory-augmented neural networks perform similarly to attentional encoders on Vietnamese to English tasks but have lower BLEU scores on Romanian to English tasks.
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.
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.
Paper aims to improve education online in South Africa using NMT for Setswana.
problem Inclusion of machine translation for low-resourced local languages in online education.
method Demonstrates state-of-the-art performance on English-to-Setswana translation using the Transformer architecture.
result Current NMT techniques show promise for African languages, beating previous methods by 5.33 BLEU points.
This paper improves neural machine translation training by selecting and denoising data.
problem Reduces negative impact of noisy data on neural machine translation training.
method Measures and selects domain data, applies denoising curriculum using online data selection.
result Significant effectiveness for training on noisy data.
Study finds optimal vocabulary size for neural machine translation.
problem Imbalanced class distribution in language data affects NMT performance.
method Casts NMT as a classification task, analyzes vocabulary sizes, and tests multiple languages.
result Certain vocabulary sizes outperform others, explaining NMT performance.
27 problems identified in automating movie/TV subtitle translation.
problem Challenges in translating movie/TV subtitles.
method Categorized problems into three categories and evaluated translation quality.
result Frontier NLP systems struggle with subtitles and require post-processing.
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.
Differentiable sampling corrects alignment issues in neural machine translation.
problem Incorrect alignment of reference words and sampled output in scheduled sampling.
method Optimizes alignment probability based on model's soft alignment prediction.
result Improves BLEU score compared to maximum likelihood and scheduled sampling.
Improves NMT with user feedback from eBay ratings and search tasks.
problem Improving neural machine translation quality with user feedback.
method Offline bandit learning of NMT parameters using real user feedback from eBay.
result Implicit task-based feedback from cross-lingual search tasks improves NMT quality.
This research explores the effects of various training settings on a Polish to English Statistical Machine Translation system for spoken language. Various elements of the TED, Europarl, and OPUS parallel text corpora were used as the basis for training of language models, for development, tuning and testing of the tran…
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…
Synthetic noise training improves machine translation robustness to spelling mistakes.
problem Making machine translation robust to spelling mistakes and natural noise.
method Training on synthetic noise to improve robustness to natural noise.
result Training on synthetic noise improves robustness to natural noise without diminishing performance on clean text.
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.