Papers with language representation model
Spelling Error Correction with Soft-Masked BERT (2020.acl-main)
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| Challenge: | Experimental results show that the proposed method is significantly better than the baselines including the one solely based on BERT. |
| Approach: | They propose a neural architecture which uses a network for error detection and a system for error correction based on BERT, with the latter connected to the other using what they call soft-masking technique. |
| Outcome: | The proposed method performs better than baselines including the one solely based on BERT, and is general and may be employed in other language detection-correction problems. |
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)
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| Challenge: | Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence. |
| Approach: | They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence. |
| Outcome: | The proposed framework achieves significant performance improvements on a large-scale benchmark dataset. |
Learning to Abstract with Nonparametric Variational Information Bottleneck (2023.findings-emnlp)
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| Challenge: | Learning textual embeddings is costly as they are tokenization specific and require different models to be trained for each level of abstraction. |
| Approach: | They propose a character-level representation model which learns different levels of abstraction in different layers of the same model. |
| Outcome: | The proposed model is more robust to adversarial perturbations and can learn to compress representations to different levels of abstraction. |
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)
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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu
| Challenge: | With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks. |
| Approach: | They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority. |
What Does BERT Learn about the Structure of Language? (P19-1)
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| Challenge: | BERT is a language representation model that has performed well in diverse language understanding benchmarks. |
| Approach: | They perform experiments to unpack the elements of English language structure learned by BERT. |
| Outcome: | The proposed model outperforms state-of-the-art models in the GLUE benchmark by a significant margin. |
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) |
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge (2020.emnlp-main)
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| Challenge: | Existing pre-trained models neglect to consider linguistic knowledge of texts . existing models neglect linguistic information, which is important for sentiment analysis . |
| Approach: | They propose a model that introduces word-level linguistic knowledge into pre-trained models to enhance sentiment analysis by querying SentiWordNet to acquire sentiment polarity. |
| Outcome: | The proposed model obtains state-of-the-art performance on a variety of sentiment analysis tasks. |
Coreferential Reasoning Learning for Language Representation (2020.emnlp-main)
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| Challenge: | Existing language representation models cannot explicitly handle coreference, which is essential to the coherent understanding of the whole discourse. |
| Approach: | They propose a language representation model that captures coreferential relations in context. |
| Outcome: | The proposed model can achieve significant improvements on downstream NLP tasks while maintaining comparable performance to baseline models on other common NLP task. |
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model (D19-1)
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| Challenge: | Existing studies on RC datasets in English have limited results due to lack of training data. |
| Approach: | They systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with pre-trained language representation model. |
| Outcome: | The proposed model performs well on reading comprehension tasks on pre-trained language representation models. |