| Challenge: | Existing approaches to probing neural networks for linguistic properties are to train a shallow multi-layer perceptron (MLP) on top of the model's internal representations. |
| Approach: | They propose a subtractive pruning-based probe where they find an existing subnetwork that performs the linguistic task of interest. |
| Outcome: | The proposed probe achieves higher accuracy on pre-trained models and lower accuracy on random models, and better learning on its own. |
Similar Papers
Information-Theoretic Probing for Linguistic Structure (2020.acl-main)
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| Challenge: | Neural networks are the backbone of modern stateof-the-art natural language processing systems. |
| Approach: | They propose an information-theoretic operationalization of probing as estimating mutual information that contradicts received wisdom . they evaluate on a set of ten typologically diverse languages often underrepresented in NLP research—plus English—totalling eleven languages. |
| Outcome: | The proposed model outperforms existing models on ten typologically diverse languages and English on 11 languages. |
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2021.eacl-main)
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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. |
| Outcome: | The proposed model can encode linguistic properties above chance-level even when distributed in the data as random noise, reversing the interpretation of absolute claims on probing tasks. |
Probe-Less Probing of BERT’s Layer-Wise Linguistic Knowledge with Masked Word Prediction (2022.naacl-srw)
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| Challenge: | Among studies on localization of linguistic knowledge, it is unclear what information is encoded in each layer. |
| Approach: | They analyze BERT’s layer-wise masked word prediction on an English corpus and find syntactic and semantic information is encoded at different layers for words of different syntaktic categories. |
| Outcome: | The proposed model outperforms state-of-the-art models in many downstream tasks. |
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs (2024.lrec-main)
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| Challenge: | a new method is being developed to probe internal linguistic characteristics in neural language models layer by layer . |
| Approach: | They propose a method that uses minimal pairs benchmark to probe internal linguistic characteristics in neural language models layer by layer. |
| Outcome: | The proposed method captures grammaticality labels in language models layer by layer . it is based on the cognitive neurosciences of the brain and its representations as "neural activations". |
Designing and Interpreting Probes with Control Tasks (D19-1)
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| Challenge: | Existing studies on supervised models to predict properties from representations have shown high accuracy on a range of linguistic tasks. |
| Approach: | They propose control tasks which associate word types with random outputs to complement linguistic tasks by construction . they find that popular probes on ELMo representations are not selective . |
| Outcome: | The proposed tasks associate word types with random outputs to complement linguistic tasks. |
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 . |
Does My Representation Capture X? Probe-Ably (2021.acl-demo)
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| Challenge: | Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. |
| Approach: | They propose to use an extendable probing framework to automate the application of probing methods to the user’s inputs. |
| Outcome: | The proposed framework automates the application of probing methods to the user’s inputs. |
Intrinsic Probing through Dimension Selection (2020.emnlp-main)
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| Challenge: | Existing research on probing for linguistic structure in word embeddings has focused on intrinsic probing, but what these representations encode about linguistic structures remains unclear. |
| Approach: | They propose a framework that allows us to determine whether linguistic information in word embeddings is dispersed or focal. |
| Outcome: | The proposed framework allows us to determine whether linguistic information in word embeddings is dispersed or focal. |
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. |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |