| Challenge: | Pretrained language models such as ELMO and XLNet have achieved state-of-the-art performance on various NLP tasks. |
| Approach: | They propose to define a layer’s role or functionality using Integrated Gradients and perform preliminary analysis across all layers. |
| Outcome: | The proposed model performs better than existing models on RCQA and ELMO, but it lacks the human-level performance needed to perform the task. |
Similar Papers
What do Models Learn from Question Answering Datasets? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models have outperformed humans on question answering datasets, but they have yet to outperform humans on the task of question answering itself. |
| Approach: | They evaluate BERT-based question answering models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. |
| Outcome: | The proposed models outperform human baselines on the widely-used SQuAD 1.1 and SQu AD 2.0 datasets. |
A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
Copied to clipboard
| Challenge: | a new study examines the current state of knowledge about the BERT model . the model is a stack of transformer encoder layers that are based on multiple self-attention ''heads'' |
| Approach: | They present a survey of over 150 studies of the popular Transformer-based model BERT . they discuss the current state of knowledge about how BERT works and how it is represented . |
| Outcome: | The proposed model is based on the Transformer-based model with state-of-the-art results . the proposed model has little cognitive motivation and is too small to perform ablation studies . |
What’s so special about BERT’s layers? A closer look at the NLP pipeline in monolingual and multilingual models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | In addition, information on part-of-speech tagging is spread over different parts of the network and the pipeline might not be as neat as it seems. |
| Approach: | They propose to probe Dutch BERT-based model and multilingual BERT model for Dutch NLP tasks to see if this holds true for other languages. |
| Outcome: | The proposed model is based on a Dutch model and a multilingual model for Dutch NLP tasks. |
Exploring the Role of BERT Token Representations to Explain Sentence Probing Results (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have focused on enhancing existing models with the primary objective of improving downstream performance on various NLP tasks. |
| Approach: | They propose to use BERT to encode meaningful knowledge in token representations to explain probing results. |
| Outcome: | The proposed model can detect syntactic and semantic abnormalities and distinguish between grammatical number and tense subspaces. |
BERT Rediscovers the Classical NLP Pipeline (P19-1)
Copied to clipboard
| Challenge: | Pre-trained text encoders have advanced the state of the art on many NLP tasks . Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically . |
| Approach: | They aim to quantify where linguistic information is captured within a network model . they aim to use pre-trained text encoders to displace static word embeddings . |
| Outcome: | The proposed model can adjust the pipeline dynamically, revealing lower-level decisions on the basis of disambiguation from higher-level representations. |
What Does BERT Learn about the Structure of Language? (P19-1)
Copied to clipboard
| 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 Has More to Offer: BERT Layers Combination Yields Better Sentence Embeddings (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Obtaining sentence representations from BERT-based models is valuable as it takes less time to pre-compute a one-time representation of the data and then use it for the downstream tasks. |
| Approach: | They propose to combine certain layers of a BERT-based model rested on the data set and model to achieve substantially better results. |
| Outcome: | The proposed method outperforms baseline models on seven semantic textual similarity datasets and on eight transfer data sets. |
Does BERT Rediscover a Classical NLP Pipeline? (2022.coling-1)
Copied to clipboard
| Challenge: | Existing theories of BERT's structure lack conclusive empirical support . however, there is scepticism about the premises of probing itself . |
| Approach: | They propose a new probe called GridLoc that can take into account token positions, training rounds, and random seeds. |
| Outcome: | The proposed probe detects other, stronger regularities suggesting appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings. |
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (P19-1)
Copied to clipboard
| Challenge: | A large number of reading comprehension (RC) datasets have been created, but little research has been done on whether they generalize to one another and the extent to which existing datasets can be leveraged for improving performance on new ones. |
| Approach: | They propose a BERT-based reading comprehension model that can be trained on multiple RC datasets. |
| Outcome: | The proposed model can be trained on multiple RC datasets and improve performance on five RC data. |
Revealing the Dark Secrets of BERT (D19-1)
Copied to clipboard
| Challenge: | Existing models of BERT-based learning systems are lacking specific mechanisms that contribute to its success. |
| Approach: | They propose to use GLUE tasks to analyze the interpretation of self-attention, which is one of the underlying components of BERT. |
| Outcome: | The proposed model outperforms the regular model on GLUE tasks by disabling attention in certain heads. |