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Found 20 papers, 11 papers with code
CMU’s IWSLT 2022 Dialect Speech Translation System
We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.
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.
CroissantLLM: A Truly Bilingual French-English Language Model
We introduce CroissantLLM, a 1. 3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.
Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues
We make use of Conditional Cross-Mutual Information (CXMI) to explore how much of the context the model uses and generalise CXMI to study the impact of the extra-sentential context.
Aligning Neural Machine Translation Models: Human Feedback in Training and Inference
A core ingredient in RLHF's success in aligning and improving large language models (LLMs) is its reward model, trained using human feedback on model outputs.
The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems.
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
Most frameworks that perform such multi-dimensional evaluation require training on large manually or synthetically generated datasets.
Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation
Recent advances in machine translation (MT) have shown that Minimum Bayes Risk (MBR) decoding can be a powerful alternative to beam search decoding, especially when combined with neural-based utility functions.
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data.
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community.
Scaling Laws for Multilingual Neural Machine Translation
Through a novel joint scaling law formulation, we compute the effective number of parameters allocated to each language pair and examine the role of language similarity in the scaling behavior of our models.
A Multi-dimensional Evaluation of Tokenizer-free Multilingual Pretrained Models
Recent work on tokenizer-free multilingual pretrained models show promising results in improving cross-lingual transfer and reducing engineering overhead (Clark et al., 2022; Xue et al., 2022).
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.
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.
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 significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics.
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.
Structured Neural Summarization
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.
