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Found 37 papers, 18 papers with code
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e. g. containing contents from multiple domains or different levels of quality or complexity.
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models.
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages.
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries.
Intriguing Properties of Compression on Multilingual Models
Multilingual models are often particularly dependent on scaling to generalize to a growing number of languages.
JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT
JoeyS2T is a JoeyNMT extension for speech-to-text tasks such as automatic speech recognition and end-to-end speech translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Building Machine Translation Systems for the Next Thousand Languages
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages.
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pre-training?
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.
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e. g. containing contents from multiple domains or different levels of quality or complexity.
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
However, evaluation of the trade-offs incurred by popular compression techniques has been centered on high-resource datasets.
Evaluating Multiway Multilingual NMT in the Turkic Languages
Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations.
Modelling Latent Translations for Cross-Lingual Transfer
To remedy this, we propose a new technique that integrates both steps of the traditional pipeline (translation and classification) into a single model, by treating the intermediate translations as a latent random variable.
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation
Policy gradient algorithms have found wide adoption in NLP, but have recently become subject to criticism, doubting their suitability for NMT.
MasakhaNER: Named Entity Recognition for African Languages
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders.
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.
Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara
Low-resource languages present unique challenges to (neural) machine translation.
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks
Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world.
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
Recent progress in text classification has been focused on high-resource languages such as English and Chinese.
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.
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages
Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved.
Correct Me If You Can: Learning from Error Corrections and Markings
Sequence-to-sequence learning involves a trade-off between signal strength and annotation cost of training data.
On Optimal Transformer Depth for Low-Resource Language Translation
Therefore, by showing that transformer models perform well (and often best) at low-to-moderate depth, we hope to convince fellow researchers to devote less computational resources, as well as time, to exploring overly large models during the development of these systems.
Joey NMT: A Minimalist NMT Toolkit for Novices
We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices.
Self-Regulated Interactive Sequence-to-Sequence Learning
Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning.
Learning to Segment Inputs for NMT Favors Character-Level Processing
Most modern neural machine translation (NMT) systems rely on presegmented inputs.
Explaining and Generalizing Back-Translation through Wake-Sleep
Back-translation has become a commonly employed heuristic for semi-supervised neural machine translation.
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning
We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT).
A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation
We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations.
Can Neural Machine Translation be Improved with User Feedback?
We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform.
Learning What's Easy: Fully Differentiable Neural Easy-First Taggers
Our models compare favourably to BILSTM taggers on three sequence tagging tasks.
A Shared Task on Bandit Learning for Machine Translation
We introduce and describe the results of a novel shared task on bandit learning for machine translation.
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning
Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback.
Stochastic Structured Prediction under Bandit Feedback
Stochastic structured prediction under bandit feedback follows a learning protocol where on each of a sequence of iterations, the learner receives an input, predicts an output structure, and receives partial feedback in form of a task loss evaluation of the predicted structure.
