Classifier Probes May Just Learn from Linear Context Features (2020.coling-main)
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| Challenge: | Current probing methods can help to better estimate the complexity of learning, but not build a foundation for speculations about the nature of the linguistic structure encoded in the learned representations. |
| Approach: | They propose to use token embeddings to test whether probing tasks contain linguistic structure . they argue that current probing methods do not provide enough information to support this hypothesis . |
| Outcome: | The proposed method can be scrutinized and proves that representations encode linguistic structure even without additional linguistic structures. |
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| Challenge: | Neural models have established state-of-the-art performance on several NLP benchmarks, but little is understood about the mechanisms by which they operate. |
| Approach: | They examine the probing paradigm through a set of controlled synthetic tasks and show that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself. |
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Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words (2020.acl-main)
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| Challenge: | a suite of probing tasks test contextual embeddings for encoding of information about surrounding words . authors: little is known about what information embeddables encode about the context words encode . a recent study shows that contextual embeds can be powerful for many tasks . |
| Approach: | They propose probing tasks that enable fine-grained testing of contextual embeddings . they examine popular contextual encoders and find that each encodes contextual information across tokens a little different . |
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
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| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
| Approach: | They propose 10 probing tasks designed to capture simple linguistic features of sentences . they use three different encoders to train embeddings in eight different ways . |
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Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction (2020.acl-main)
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| Challenge: | Neural relation extraction models capture linguistic and semantic properties of the input, a recent study shows. |
| Approach: | They introduce 14 probing tasks targeting linguistic properties relevant to RE . they add contextualized word representations to enhance probing performance . |
| Outcome: | The proposed models achieve state-of-the-art on two datasets, TACRED and SemEval 2010 Task 8 . they show that the models capture linguistic and semantic properties relevant to the downstream task . |
Probing Representations for Document-level Event Extraction (2023.findings-emnlp)
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| Challenge: | Document-level information extraction tasks require a more comprehensive understanding that often extends to the entire input document. |
| Approach: | They propose to use probing to analyze document-level information extraction representations by embedding probes into a standard dataset. |
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Probing for Constituency Structure in Neural Language Models (2022.findings-emnlp)
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| Challenge: | Using standard probing techniques, we examine whether contextual neural language models implicitly learn syntactic structure. |
| Approach: | They investigate to which extent contextual neural language models implicitly learn syntactic structure. |
| Outcome: | The proposed model is able to represent constituents of different categories within the neuron activations of a LM such as RoBERTa with high performance even on manipulated data. |
Exploring the Role of BERT Token Representations to Explain Sentence Probing Results (2021.emnlp-main)
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| 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. |
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A Tale of a Probe and a Parser (2020.acl-main)
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| Challenge: | researchers train supervised models to extract linguistic structure from output of another model . supervised model can be used to perform tasks such as part-of-speech tags or dependency trees . |
| Approach: | They compare a structural probe to a more traditional parser with a lightweight parameterisation. |
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Asking without Telling: Exploring Latent Ontologies in Contextual Representations (2020.emnlp-main)
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| Challenge: | Recent work on model analysis indicates that they may learn a lot about linguistic structure, including part of speech, syntax, word sense, and more. |
| Approach: | They introduce latent subclass learning, a modification to classifier-based probing that induces a latent categorization (or ontology) of the probe’s inputs. |
| Outcome: | The proposed model induces a latent categorization (or ontology) of the probe’s inputs without access to fine-grained gold labels. |
A Non-Linear Structural Probe (2021.naacl-main)
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| Challenge: | Probing is a method of investigating the encoding of knowledge in contextual representations. |
| Approach: | They propose to kernelize a metric and develop a non-linear variant with an identical number of parameters by using a kernel-based probe. |
| Outcome: | The proposed probe learns only linear transformations and achieves statistically significant performance improvement over baseline in all languages. |