| Challenge: | Recent work has explored contextual word representations, which assign each word a vector that is a function of the entire input sequence. |
| Approach: | They compare pretrained word representations with 16 diverse probing tasks to examine their transferability. |
| Outcome: | The pretrained representations are successful across a diverse set of NLP tasks . the models are competitive with state-of-the-art models but fail on fine-grained tasks requiring fine-granular knowledge, the study finds . |
Similar Papers
Quantifying the Contextualization of Word Representations with Semantic Class Probing (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Pretrained language models are effective in solving NLP tasks, but there are still questions about how and why they work so well. |
| Approach: | They use BERT to quantify contextualization by studying the extent of inference . they show that top layer representations support highly accurate inference of semantic classes . |
| Outcome: | The proposed model is highly accurate, but weak in the lower layers . it is more task-specific after finetuning while lower layers are more transferable . |
How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings (D19-1)
Copied to clipboard
| Challenge: | Existing word embeddings were static, requiring all senses of a polysemous word to share the same representation. |
| Approach: | They found that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. |
| Outcome: | The results show that the representations of all words are not isotropic in any layer of the contextualizing model. |
Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies show that pretraining with an artificial language with nesting dependency structure provides some knowledge transferable to natural language. |
| Approach: | They propose to pretrain artificial languages with structural properties that mimic natural language and then test their performance on downstream tasks. |
| Outcome: | The proposed language models show strong performance across languages and languages. |
Probing Pretrained Language Models for Lexical Semantics (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have focused on morphosyntactic, semantic, and world knowledge, but it remains unclear to what extent LMs derive lexical type-level knowledge from words in context. |
| Approach: | They propose to use multilingual and monolingual LMs to extract lexical type-level knowledge from words in context. |
| Outcome: | The proposed models perform well across six typologically diverse languages and five lexical tasks. |
Investigating Transferability in Pretrained Language Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on deep NLP models has centered on probing, a method that involves training classifiers for different tasks on model representations. |
| Approach: | They propose a method for determining the impact of each pretrained layer on transfer task performance by ablation. |
| Outcome: | The proposed method shows that pretraining models improve performance on downstream tasks . the results highlight the limitations of methods that operate on frozen models or single data samples. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
Copied to clipboard
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
Which *BERT? A Survey Organizing Contextualized Encoders (2020.emnlp-main)
Copied to clipboard
| Challenge: | a survey on language representation learning aims to highlight common themes . we focus on the areas of progress, compared to other fields, and discuss how each area is evaluated. |
| Approach: | They present a survey on language representation learning to highlight common themes . they compare contributions in contextualized text encoders to ideas from other fields . |
| Outcome: | The proposed survey aims to highlight common themes in the field of language representation learning. |
Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (D19-60)
Copied to clipboard
| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
| Approach: | They investigate BERT's ability to encode various commonsense features in its embedding space, but are still deficient in many areas. |
| Outcome: | The proposed model improves performance on a downstream commonsense reasoning task while using minimal data. |
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks (2022.naacl-main)
Copied to clipboard
| Challenge: | a recent study examines the features and limits of LM adaptability to new tasks . many questions about the nature and limits remain unanswered . |
| Approach: | They evaluate adaptability to new tasks using a new benchmark, TaskBench500 . they find adaptation procedures differ dramatically in their ability to memorize small datasets . |
| Outcome: | The proposed benchmark compares 500 procedurally generated sequence modeling tasks to a new benchmark. |
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings (2020.acl-main)
Copied to clipboard
| Challenge: | Contextualized representations have become the default for downstream NLP applications. |
| Approach: | They propose a method for converting from contextualized representations to static lookup-table embeddings and apply it to 5 popular pretrained models and 9 sets of pretrained weights. |
| Outcome: | The proposed methods show that pooling over many contexts significantly improves representational quality under intrinsic evaluation. |