Papers with machine translation benchmarks
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. |
| Approach: | They propose a generative paradigm for translation tasks that integrates the diverse translation versions in N-best list. |
| Outcome: | The proposed model outperforms the state-of-the-art model on speech and machine translation benchmarks on various languages. |
LightSeq: A High Performance Inference Library for Transformers (2021.naacl-industry)
Copied to clipboard
| Challenge: | Existing inference frameworks for natural language processing are not the best choice for online service of sequence processing problems. |
| Approach: | They propose a highly efficient inference library for Transformer models that includes GPU optimization techniques to streamline computation and reduce memory footprint. |
| Outcome: | The proposed library achieves 14x speedup compared with TensorFlow and 1.4x speed up compared to a concurrent CUDA implementation. |
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation (2022.findings-acl)
Copied to clipboard
| Challenge: | Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks. |
| Approach: | They propose to deepen the decoder layer in a Transformer model to reduce the difficulty of deep learning. |
| Outcome: | The proposed method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance. |
Consistency Regularization Training for Compositional Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing neural models have difficulty generalizing to unseen combinations of seen components. |
| Approach: | They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures. |
| Outcome: | The proposed model performs well on semantic parsing and machine translation benchmarks. |
Efficient Cluster-Based k-Nearest-Neighbor Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | k-Nearest-Neighbor Machine Translation (kNN-MT) is a non-parametric solution for domain adaptation . previous studies have shown that kNN retrieval is at the expense of high latency . |
| Approach: | They propose to use clustering to improve retrieval efficiency by combining a non-parametric MT with an in-domain feature-based retrieval module. |
| Outcome: | The proposed method reduces translation latency by 57% while maintaining the most useful information of the original datastore. |
What Do Compressed Multilingual Machine Translation Models Forget? (2022.findings-emnlp)
Copied to clipboard
Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
| Challenge: | Recent studies show that pre-trained models achieve state-of-the-art results in NLP tasks but their size makes it more challenging to apply them in resource-constrained environments. |
| Approach: | They assess the impact of compression methods on multilingual Neural Machine Translation models for various language groups, gender, and semantic biases. |
| Outcome: | The proposed compression methods improve models on different benchmarks for language groups, gender, and semantic biases. |
Training Language Models with Memory Augmentation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for training memory-augmented language models only introduce mem-ories at testing time or represent them using a separately trained encoder. |
| Approach: | They propose a training approach that directly takes in-batch examples as accessible memory and new methods for memory construction and data batching that are used for adapting to different sets of memories at testing time. |
| Outcome: | The proposed approach reduces perplexity from 18.70 to 15.37 on multiple language modeling and machine translation benchmarks. |
TranSFormer: Slow-Fast Transformer for Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Prior work has focused on treating subwords as basic units in developing such systems. |
| Approach: | They propose a slow-fast two-stream learning model that uses a “slow” branch to deal with subword sequences and a "fast" branch to cope with longer character sequences. |
| Outcome: | The proposed model shows consistent BLEU improvements (larger than 1 BLUE point) on several machine translation benchmarks. |
Guiding Neural Machine Translation with Semantic Kernels (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Empirical studies show that our approach gains approximately an improvement of 1 BLEU score on most benchmarks over the Transformer baseline. |
| Approach: | They propose to extract several semantic kernels from a source sentence to capture global semantic information. |
| Outcome: | Empirical results show that the proposed approach improves 1 BLEU score on benchmarks . it is also 1.7 times faster than previous works on average at inference time . |