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Found 37 papers, 6 papers with code
Bilingual Methods for Adaptive Training Data Selection for Machine Translation
In this paper, we propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus.
A Natural Diet: Towards Improving Naturalness of Machine Translation Output
In this work we propose a method for training MT systems to achieve a more natural style, i. e. mirroring the style of text originally written in the target language.
Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain
Contrary to previous years’ editions, this year we acquired our own human ratings based on expert-based human evaluation via Multidimensional Quality Metrics (MQM).
Finding Replicable Human Evaluations via Stable Ranking Probability
Reliable human evaluation is critical to the development of successful natural language generation models, but achieving it is notoriously difficult.
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation
Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart.
Adapting Large Language Models for Document-Level Machine Translation
Large language models (LLMs) have made significant strides in various natural language processing (NLP) tasks.
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation
We conduct a large-scale fine-grained comparative analysis of machine translations (MT) against human translations (HT) through the lens of morphosyntactic divergence.
Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration
Kendall's tau is frequently used to meta-evaluate how well machine translation (MT) evaluation metrics score individual translations.
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability
We investigate the role of incidental bilingualism -- the unintentional consumption of bilingual signals, including translation examples -- in explaining the translation capabilities of large language models, taking the Pathways Language Model (PaLM) as a case study.
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation
Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.
The unreasonable effectiveness of few-shot learning for machine translation
We demonstrate the potential of few-shot translation systems, trained with unpaired language data, for both high and low-resource language pairs.
Prompting PaLM for Translation: Assessing Strategies and Performance
Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages.
Toward More Effective Human Evaluation for Machine Translation
Improvements in text generation technologies such as machine translation have necessitated more costly and time-consuming human evaluation procedures to ensure an accurate signal.
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions.
Assessing Reference-Free Peer Evaluation for Machine Translation
Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains.
Human-Paraphrased References Improve Neural Machine Translation
When used in place of original references, the paraphrased versions produce metric scores that correlate better with human judgment.
Inference Strategies for Machine Translation with Conditional Masking
Conditional masked language model (CMLM) training has proven successful for non-autoregressive and semi-autoregressive sequence generation tasks, such as machine translation.
Re-translation versus Streaming for Simultaneous Translation
There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available.
Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation
As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows.
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair.
Thinking Slow about Latency Evaluation for Simultaneous Machine Translation
Simultaneous machine translation attempts to translate a source sentence before it is finished being spoken, with applications to translation of spoken language for live streaming and conversation.
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT).
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives.
Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering.
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures.
Ranked #26 on
Machine Translation
on WMT2014 English-French
Cost Weighting for Neural Machine Translation Domain Adaptation
We compare cost weighting to two traditional domain adaptation techniques developed for statistical machine translation: data selection and sub-corpus weighting.
A Challenge Set Approach to Evaluating Machine Translation
We address these questions with a challenge set approach to translation evaluation and error analysis.
