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Found 14 papers, 5 papers with code
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach
Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.
Learning How to Active Learn by Dreaming
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
Active Continual Learning: On Balancing Knowledge Retention and Learnability
Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL).
Simultaneous Machine Translation with Large Language Models
The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
Systematic Assessment of Factual Knowledge in Large Language Models
We systematically evaluate the state-of-the-art LLMs with KGs in generic and specific domains.
Adapting Large Language Models for Document-Level Machine Translation
Large language models (LLMs) have made significant strides in various natural language processing (NLP) tasks.
Continual Learning for Large Language Models: A Survey
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale.
Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency.
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Generalising to unseen domains is under-explored and remains a challenge in neural machine translation.
