What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models (2020.tacl-1)
Copied to clipboard
| Challenge: | Pretraining by language modeling has become popular but we have yet to understand what language models learn during that process. |
| Approach: | They propose diagnostics that ask questions about information used by language models for generating predictions in context. |
| Outcome: | The proposed diagnostics can be used to study the popular BERT model . they show that the model can distinguish good from bad completions, but struggles with inference and role-based event prediction. |
Similar Papers
How does the pre-training objective affect what large language models learn about linguistic properties? (2022.acl-short)
Copied to clipboard
| Challenge: | Several pre-training objectives have been proposed to pre-train language models . but, to our knowledge, no studies have investigated how different pre- training objectives affect what BERT learns about linguistic properties. |
| Approach: | They propose to use masked language modeling to pre-train language models . they propose to optimize a mangled language modeling objective to learn linguistic information . |
| Outcome: | The proposed objectives improve BERT's learning of linguistic properties compared to non-linguistically motivated objectives. |
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. |
From BERT‘s Point of View: Revealing the Prevailing Contextual Differences (2022.findings-acl)
Copied to clipboard
| Challenge: | BERTology is a new approach to understanding the inner workings of large pretraining language models. |
| Approach: | They propose to invert the probing design to analyze the prevailing differences and clusters in BERT’s high dimensional space by extracting coarse features from masked token representations and predicting them by probing models with access to only partial information. |
| Outcome: | The proposed method extracts coarse features from masked token representations and predicts them by probing models with access to only partial information. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
Copied to clipboard
Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
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 . |
Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
Copied to clipboard
| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
| Outcome: | The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks. |
On the use of BERT for Neural Machine Translation (D19-56)
Copied to clipboard
| 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. |
How does BERT’s attention change when you fine-tune? An analysis methodology and a case study in negation scope (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work probing pre-trained language models for downstream tasks is difficult to explain . a growing body of research is devoted to understanding what linguistic properties these language models have acquired. |
| Approach: | They propose a procedure and analysis method that takes a hypothesis of how a transformer-based model might encode a linguistic phenomenon and tests its validity. |
| Outcome: | The proposed method tests a hypothesis that some attention heads will consistently attend from a word in negation scope to the negation cue. |
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing evidence suggests that pre-trained Transformers encode commonsense knowledge . however, the extent to which this knowledge is acquired is unclear . |
| Approach: | They inject verbalized knowledge into pre-training minibatches and evaluate generalization . they find generalization does not improve over the course of pre- training from scratch . |
| Outcome: | The proposed model generalizes to supported inferences after pre-training on the injected knowledge. |
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. |