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Found 28 papers, 12 papers with code
Mitigating Data Poisoning in Text Classification with Differential Privacy
NLP models are vulnerable to data poisoning attacks.
Findings of the WMT 2021 Shared Task on Large-Scale Multilingual Machine Translation
There were a total of 10 participating teams for the tasks, with a total of 151 intermediate model submissions and 13 final models.
BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task
We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems.
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 Parallel Corpus Filtering and Alignment
Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems.
Findings of the WMT 2020 Shared Task on Machine Translation Robustness
We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT).
SeamlessM4T-Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages?
Automatic Speech Recognition
Speech-to-Speech Translation
+3
No Language Left Behind: Scaling Human-Centered Machine Translation
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
Ranked #1 on
Machine Translation
on IWSLT2015 English-Vietnamese
(SacreBLEU metric)
OCR Improves Machine Translation for Low-Resource Languages
We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts.
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications
Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels.
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models
Cross-lingual document representations enable language understanding in multilingual contexts and allow transfer learning from high-resource to low-resource languages at the document level.
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages.
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment
Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification.
Quality Estimation without Human-labeled Data
Quality estimation aims to measure the quality of translated content without access to a reference translation.
Improving Zero-Shot Translation by Disentangling Positional Information
The difficulty of generalizing to new translation directions suggests the model representations are highly specific to those language pairs seen in training.
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).
TICO-19: the Translation Initiative for Covid-19
Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.
Unsupervised Quality Estimation for Neural Machine Translation
Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time.
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance
Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other.
Machine Translation Evaluation Meets Community Question Answering
We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering.
Unsupervised Cross-lingual Representation Learning at Scale
We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale.
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
Pre-training text representations have led to significant improvements in many areas of natural language processing.
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 85 languages, including several dialects or low-resource languages.
Low-Resource Corpus Filtering using Multilingual Sentence Embeddings
In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task.
The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.
Machine Translation Evaluation with Neural Networks
We present a framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation.
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation.
