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Incorporating Constituent Syntax for Coreference Resolution ...
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PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation ...
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As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ...
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Balancing out Bias: Achieving Fairness Through Training Reweighting ...
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ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents
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In: Front Res Metr Anal (2021)
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Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning ...
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Learning a Cost-Effective Annotation Policy for Question Answering
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Speakers Fill Lexical Semantic Gaps with Context
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Intrinsic Probing through Dimension Selection
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Abstract:
Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted. To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal. We then probe fastText and BERT for various morphosyntactic attributes across 36 languages. We find that most attributes are reliably encoded by only a few neurons, with fastText concentrating its linguistic structure more than BERT.
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URL: https://doi.org/10.3929/ethz-b-000462314 https://hdl.handle.net/20.500.11850/462314
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Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
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In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020)
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Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
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