| Challenge: | In monolingual tasks, the number of unlearned model parameters is as huge as the number learned parameters in the BERT model. |
| Approach: | They propose to apply a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to Transformer-based neural machine translation (NMT) based on the Transformer. |
| Outcome: | The proposed model is stable and efficient in low-resource settings. |
Similar Papers
On the use of BERT for Neural Machine Translation (D19-56)
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| Challenge: | Existing studies on using pretrained language models for supervised NMT have not been successful. |
| Approach: | They propose to integrate BERT pretrained models with supervised NMT models by using monolingual data. |
| Outcome: | The proposed models improve translation quality in English-German, English-Russian and IWSLT14 datasets. |
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (N19-1)
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| Challenge: | Existing language representation models pre-train deep bidirectional representations from unlabeled text without significant task-specific architecture modifications. |
| Approach: | They propose a language representation model that pre-trains bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. |
| Outcome: | The proposed model achieves state-of-the-art results on eleven natural language processing tasks, pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement) |
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing methods for incorporating pre-trained models into NMT systems are non-trivial and lack a comparison of the impact that other pre-trainers may have on translation performance. |
| Approach: | They propose to use the input of a bilingual pre-trained language model as the input for NMT encoders and a stochastic layer selection approach to ensure sufficient utilization of contextualized embeddings. |
| Outcome: | The proposed bilingual pre-trained language model outperforms all other pre-train models on the IWSLT’14 dataset and the proposed dual-directional translation model. |
Multilingual Translation via Grafting Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to graft pre-trained (masked) language models to multilingual data are limited, and they lack cross-attention component. |
| Approach: | They propose to graft separately pre-trained (masked) language models for machine translation using monolingual data and parallel data. |
| Outcome: | The proposed method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLUE in en2x directions compared with the multilingual Transformer of the same size. |
Document-Level Neural Machine Translation Using BERT as Context Encoder (2020.aacl-srw)
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| Challenge: | Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. |
| Approach: | They propose to use BERT as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. |
| Outcome: | The proposed model outperforms strong document-level machine translation baselines on BLEU score and captures document- level context information to boost translation performance. |
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT (2021.tacl-1)
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Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
| Challenge: | Popular pre-trained Transformers have improved performance for various NLP tasks by sizable margins, but are too resource-hungry and computation-intensive to suit low-capacity devices or applications with strict latency requirements. |
| Approach: | They present a literature review of the compression of Transformers, focusing on the popular BERT model, which has attracted considerable research attention. |
| Outcome: | The proposed models improve Sentiment analysis, paraphrase detection, machine reading comprehension, question answering, text summarization, and other tasks by sizable margins. |
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees (2021.eacl-main)
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| Challenge: | Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. |
| Approach: | They propose a plug-and-play framework that incorporates syntax trees into pre-trained Transformers. |
| Outcome: | The proposed framework improves on pre-trained models on natural language understanding datasets and shows that it can be used to train pre-structured neural networks. |
A Comparison between Pre-training and Large-scale Back-translation for Neural Machine Translation (2021.findings-acl)
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| Challenge: | BERT is a promising technique to improve NMT, but how it outperforms standard NMT is understudied. |
| Approach: | We compare MT engines trained with pre-trained BERT and back-translation with incrementally larger amounts of data. |
| Outcome: | The proposed technique outperforms standard NMT models on morphology and syntax. |
Text Summarization with Pretrained Encoders (D19-1)
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| Challenge: | Existing pretraining languages such as ELMo and GPT have advanced a wide range of tasks. |
| Approach: | They propose a novel document-level encoder based on BERT which can express the semantics of a document and obtain representations for its sentences. |
| Outcome: | The proposed model achieves state-of-the-art in extractive and abstractive settings. |
Encoder and Decoder, Not One Less for Pre-trained Language Model Sponsored NMT (2023.findings-acl)
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| Challenge: | Existing methods for neural machine translation (NMT) use encoder-only enhancement or rely on specific multilingual PLMs. |
| Approach: | They propose a monolingual PLM-sponsored NMT model that lets both encoder and decoder enjoy PLM enhancement. |
| Outcome: | The proposed model improves on IWSLT14 En-De, De-En, WMT14 En -De and En-Fr tasks and achieves new state-of-the-art. |