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) |
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| Challenge: | Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. |
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Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning (2020.acl-main)
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| Challenge: | Existing deep bidirectional language models are limited by repetitive inferences on unsupervised tasks for the computation of contextual language representations. |
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Recycling a Pre-trained BERT Encoder for Neural Machine Translation (D19-56)
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| Challenge: | In monolingual tasks, the number of unlearned model parameters is as huge as the number learned parameters in the BERT model. |
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Adaptation of Deep Bidirectional Transformers for Afrikaans Language (2020.lrec-1)
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Pretrained Transformers for Text Ranking: BERT and Beyond (2021.naacl-tutorials)
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| Challenge: | This tutorial provides an overview of text ranking using neural network architectures known as transformers. |
| Approach: | This tutorial provides an overview of text ranking with neural network architectures known as transformers. |
| Outcome: | This tutorial provides an overview of text ranking with neural network architectures known as transformers. |
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. |
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Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification (D19-50)
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| Challenge: | a team of researchers used a pre-trained BERT language model to train propaganda . the model was based on a cloze comprehension test to answer a question about influence operations . |
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schuBERT: Optimizing Elements of BERT (2020.acl-main)
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| Challenge: | Recent Transformer based models have achieved state-of-the-art performance for many natural language processing tasks including machine translation, question-answering tasks and semantic role labeling. |
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LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention (2020.emnlp-main)
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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. |
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