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Probing Classifiers: Promises, Shortcomings, and Advances ...
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On the Pitfalls of Analyzing Individual Neurons in Language Models ...
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Debiasing Methods in Natural Language Understanding Make Bias More Accessible ...
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Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ...
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Similarity Analysis of Contextual Word Representation Models ...
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Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? ...
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The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ...
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Analyzing Individual Neurons in Pre-trained Language Models ...
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On the Linguistic Representational Power of Neural Machine Translation Models
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In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020)
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Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
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In: MIT Press (2019)
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On the Linguistic Representational Power of Neural Machine Translation Models ...
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On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference ...
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Improving Neural Language Models by Segmenting, Attending, and Predicting the Future ...
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Abstract:
Common language models typically predict the next word given the context. In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase. The model does not require any linguistic annotation of phrase segmentation. Instead, we define syntactic heights and phrase segmentation rules, enabling the model to automatically induce phrases, recognize their task-specific heads, and generate phrase embeddings in an unsupervised learning manner. Our method can easily be applied to language models with different network architectures since an independent module is used for phrase induction and context-phrase alignment, and no change is required in the underlying language modeling network. Experiments have shown that our model outperformed several strong baseline models on different data sets. We achieved a new state-of-the-art performance of 17.4 perplexity on the Wikitext-103 dataset. Additionally, visualizing the outputs of the phrase induction module ... : Accepted by ACL 2019 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/1906.01702 https://dx.doi.org/10.48550/arxiv.1906.01702
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On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
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On Evaluating the Generalization of LSTM Models in Formal Languages
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Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
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On Evaluating the Generalization of LSTM Models in Formal Languages
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Analysis Methods in Neural Language Processing: A Survey
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 49-72 (2019) (2019)
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