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Found 31 papers, 13 papers with code
A Context-aware Framework for Translation-mediated Conversations
In this work, we present a framework to improve large language model-based translation systems by incorporating contextual information in bilingual conversational settings.
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings
Reinforcement learning (RL) has been proven to be an effective and robust method for training neural machine translation systems, especially when paired with powerful reward models that accurately assess translation quality.
Findings of the WMT 2024 Shared Task on Chat Translation
This paper presents the findings from the third edition of the Chat Translation Shared Task.
Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation
Focusing on out-of-English translations into languages with grammatical gender, we ask: Do contemporary QE metrics exhibit gender bias?
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation
Alignment with human preferences is an important step in developing accurate and safe large language models.
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies.
The Prompt Report: A Systematic Survey of Prompting Techniques
Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains.
QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
An important challenge in machine translation (MT) is to generate high-quality and diverse translations.
Can Automatic Metrics Assess High-Quality Translations?
Automatic metrics for evaluating translation quality are typically validated by measuring how well they correlate with human assessments.
Is Context Helpful for Chat Translation Evaluation?
Our findings show that augmenting neural learned metrics with contextual information helps improve correlation with human judgments in the reference-free scenario and when evaluating translations in out-of-English settings.
Tower: An Open Multilingual Large Language Model for Translation-Related Tasks
While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task.
Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension
With this framework, we conduct a thorough human evaluation of texts by humans and by nine automatic systems.
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments.
Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors
A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs.
BLESS: Benchmarking Large Language Models on Sentence Simplification
Our performance benchmark will be available as a resource for the development of future TS methods and evaluation metrics.
Controlling Pre-trained Language Models for Grade-Specific Text Simplification
Based on these insights, we introduce a simple method that predicts the edit operations required for simplifying a text for a specific grade level on an instance-per-instance basis.
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
Neural sequence generation models are known to "hallucinate", by producing outputs that are unrelated to the source text.
In-context Examples Selection for Machine Translation
Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used to describe a task to the model.
Controlling Translation Formality Using Pre-trained Multilingual Language Models
This paper describes the University of Maryland's submission to the Special Task on Formality Control for Spoken Language Translation at \iwslt, which evaluates translation from English into 6 languages with diverse grammatical formality markers.
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models
We propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output.
The two body problem: proprioception and motor control across the metamorphic divide
Like a rocket being propelled into space, evolution has engineered flies to launch into adulthood via multiple stages.
Can Multilinguality benefit Non-autoregressive Machine Translation?
Non-autoregressive (NAR) machine translation has recently achieved significant improvements, and now outperforms autoregressive (AR) models on some benchmarks, providing an efficient alternative to AR inference.
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer
While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation.
A Review of Human Evaluation for Style Transfer
However, in style transfer papers, we find that protocols for human evaluations are often underspecified and not standardized, which hampers the reproducibility of research in this field and progress toward better human and automatic evaluation methods.
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.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
Generating Diverse Translations via Weighted Fine-tuning and Hypotheses Filtering for the Duolingo STAPLE Task
This paper describes the University of Maryland{'}s submission to the Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE).
Multitask Models for Controlling the Complexity of Neural Machine Translation
We introduce a machine translation task where the output is aimed at audiences of different levels of target language proficiency.
Controlling Text Complexity in Neural Machine Translation
This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency.
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
We show that deep learning based models can overcome all three bottlenecks.
