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Found 58 papers, 39 papers with code
QUARTZ: Quality-Aware Machine Translation
This paper presents QUARTZ, QUality-AwaRe machine Translation, a project led by Unbabel which aims at developing machine translation systems that are more robust and produce fewer critical errors.
IST-Unbabel 2021 Submission for the Quality Estimation Shared Task
We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation.
Findings of the WMT 2021 Shared Task on Quality Estimation
We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels.
IST-Unbabel Participation in the WMT20 Quality Estimation Shared Task
We present the joint contribution of IST and Unbabel to the WMT 2020 Shared Task on Quality Estimation.
Findings of the WMT 2020 Shared Task on Quality Estimation
We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels.
Findings of the WMT 2020 Shared Task on Chat Translation
We report the results of the first edition of the WMT shared task on chat translation.
Project MAIA: Multilingual AI Agent Assistant
This paper presents the Multilingual Artificial Intelligence Agent Assistant (MAIA), a project led by Unbabel with the collaboration of CMU, INESC-ID and IT Lisbon.
DeepSPIN: Deep Structured Prediction for Natural Language Processing
DeepSPIN is a research project funded by the European Research Council (ERC) whose goal is to develop new neural structured prediction methods, models, and algorithms for improving the quality, interpretability, and data-efficiency of natural language processing (NLP) systems, with special emphasis on machine translation and quality estimation applications.
Document-level Neural MT: A Systematic Comparison
In this paper we provide a systematic comparison of existing and new document-level neural machine translation solutions.
Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task
In this paper, we present the joint contribution of Unbabel and IST to the WMT 2021 Metrics Shared Task.
Chunk-based Nearest Neighbor Machine Translation
Experiments on machine translation in two settings, static domain adaptation and ``on-the-fly'' adaptation, show that the chunk-based $k$NN-MT model leads to a significant speed-up (up to 4 times) with only a small drop in translation quality.
Quality-Aware Decoding for Neural Machine Translation
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search.
Efficient Machine Translation Domain Adaptation
On the other hand, semi-parametric models have been shown to successfully perform domain adaptation by retrieving examples from an in-domain datastore (Khandelwal et al., 2021).
Learning to Scaffold: Optimizing Model Explanations for Teaching
In this work, leveraging meta-learning techniques, we extend this idea to improve the quality of the explanations themselves, specifically by optimizing explanations such that student models more effectively learn to simulate the original model.
Better Uncertainty Quantification for Machine Translation Evaluation
Neural-based machine translation (MT) evaluation metrics are progressing fast.
Differentiable Causal Discovery Under Latent Interventions
Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown.
Modeling Structure with Undirected Neural Networks
In this paper, we combine the representational strengths of factor graphs and of neural networks, proposing undirected neural networks (UNNs): a flexible framework for specifying computations that can be performed in any order.
Predicting Attention Sparsity in Transformers
Transformers' quadratic complexity with respect to the input sequence length has motivated a body of work on efficient sparse approximations to softmax.
When Does Translation Require Context? A Data-driven, Multilingual Exploration
Although proper handling of discourse phenomena significantly contributes to the quality of machine translation (MT), common translation quality metrics do not adequately capture them.
Uncertainty-Aware Machine Translation Evaluation
Several neural-based metrics have been recently proposed to evaluate machine translation quality.
SPECTRA: Sparse Structured Text Rationalization
Selective rationalization aims to produce decisions along with rationales (e. g., text highlights or word alignments between two sentences).
$\infty$-former: Infinite Memory Transformer
Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length.
Ranked #1 on
Dialogue Generation
on WikiText-103
Sparse Communication via Mixed Distributions
Neural networks and other machine learning models compute continuous representations, while humans communicate mostly through discrete symbols.
Sparse Continuous Distributions and Fenchel-Young Losses
When $\Omega$ is a Tsallis negentropy with parameter $\alpha$, we obtain "deformed exponential families," which include $\alpha$-entmax and sparsemax ($\alpha$ = 2) as particular cases.
Do Context-Aware Translation Models Pay the Right Attention?
Are models paying large amounts of attention to the same context?
Measuring and Increasing Context Usage in Context-Aware Machine Translation
Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context -- context from sentences other than those currently being translated.
Multimodal Continuous Visual Attention Mechanisms
Visual attention mechanisms are a key component of neural network models for computer vision.
Reconciling the Discrete-Continuous Divide: Towards a Mathematical Theory of Sparse Communication
Neural networks and other machine learning models compute continuous representations, while humans communicate with discrete symbols.
Smoothing and Shrinking the Sparse Seq2Seq Search Space
Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences.
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning
Latent structure models are a powerful tool for modeling language data: they can mitigate the error propagation and annotation bottleneck in pipeline systems, while simultaneously uncovering linguistic insights about the data.
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
In this paper, we propose a new training strategy which replaces these estimators by an exact yet efficient marginalization.
Sparse and Continuous Attention Mechanisms
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e. g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation).
Ranked #28 on
Visual Question Answering
on VQA v2 test-dev
The Explanation Game: Towards Prediction Explainability through Sparse Communication
Explainability is a topic of growing importance in NLP.
Sparse Text Generation
Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance.
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Structured prediction requires manipulating a large number of combinatorial structures, e. g., dependency trees or alignments, either as latent or output variables.
Adaptively Sparse Transformers
Findings of the quantitative and qualitative analysis of our approach include that heads in different layers learn different sparsity preferences and tend to be more diverse in their attention distributions than softmax Transformers.
Translator2Vec: Understanding and Representing Human Post-Editors
The combination of machines and humans for translation is effective, with many studies showing productivity gains when humans post-edit machine-translated output instead of translating from scratch.
Notes on Latent Structure Models and SPIGOT
These notes aim to shed light on the recently proposed structured projected intermediate gradient optimization technique (SPIGOT, Peng et al., 2018).
Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task
We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation.
Joint Learning of Named Entity Recognition and Entity Linking
Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected.
Ranked #6 on
Entity Linking
on AIDA-CoNLL
Scheduled Sampling for Transformers
In the Transformer model, unlike the RNN, the generation of a new word attends to the full sentence generated so far, not only to the last word, and it is not straightforward to apply the scheduled sampling technique.
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing
Analogously to dual-encoder architectures we develop a BERT-based encoder-decoder (BED) model in which a single pretrained BERT encoder receives both the source src and machine translation tgt strings.
Jointly Extracting and Compressing Documents with Summary State Representations
We present a new neural model for text summarization that first extracts sentences from a document and then compresses them.
Selective Attention for Context-aware Neural Machine Translation
Despite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document.
OpenKiwi: An Open Source Framework for Quality Estimation
We introduce OpenKiwi, a PyTorch-based open source framework for translation quality estimation.
Learning with Fenchel-Young Losses
Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction.
Towards Dynamic Computation Graphs via Sparse Latent Structure
Deep NLP models benefit from underlying structures in the data---e. g., parse trees---typically extracted using off-the-shelf parsers.
Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
In this work, we propose the task of translating Bilingual Multi-Speaker Conversations, and explore neural architectures which exploit both source and target-side conversation histories for this task.
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms
This paper studies Fenchel-Young losses, a generic way to construct convex loss functions from a regularization function.
Sparse and Constrained Attention for Neural Machine Translation
In NMT, words are sometimes dropped from the source or generated repeatedly in the translation.
Marian: Fast Neural Machine Translation in C++
We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs.
SparseMAP: Differentiable Sparse Structured Inference
Structured prediction requires searching over a combinatorial number of structures.
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities.
