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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 The Limitations of Limited Context for Constituency Parsing ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Li, Yuchen; Risteski, Andrej. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.208 Abstract: Incorporating syntax into neural approaches in NLP has a multitude of practical and scientific benefits. For instance, a language model that is syntax-aware is likely to be able to produce better samples; even a discriminative model like BERT with a syntax module could be used for core NLP tasks like unsupervised syntactic parsing. Rapid progress in recent years was arguably spurred on by the empirical success of the Parsing-Reading-Predict architecture of (Shen et al., 2018a), later simplified by the Order Neuron LSTM of (Shen et al., 2019). Most notably, this is the first time neural approaches were able to successfully perform unsupervised syntactic parsing (evaluated by various metrics like F-1 score). However, even heuristic (much less fully mathematical) understanding of why and when these architectures work is lagging severely behind. In this work, we answer representational questions raised by the architectures in (Shen et al., ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25536-the-limitations-of-limited-context-for-constituency-parsing https://dx.doi.org/10.48448/05a7-qs41 BASE Hide details 2 On Learning Language-Invariant Representations for Universal Machine Translation ... Zhao, Han; Hu, Junjie; Risteski, Andrej. - : arXiv, 2020 BASE Show details 3 Extending and Improving Wordnet via Unsupervised Word Embeddings ... Khodak, Mikhail; Risteski, Andrej; Fellbaum, Christiane. - : arXiv, 2017 BASE Show details 4 Linear Algebraic Structure of Word Senses, with Applications to Polysemy ... Arora, Sanjeev; Li, Yuanzhi; Liang, Yingyu. - : arXiv, 2016 BASE Show details 5 A Latent Variable Model Approach to PMI-based Word Embeddings ... Arora, Sanjeev; Li, Yuanzhi; Liang, Yingyu. - : arXiv, 2015 BASE Show details

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