Efficient Constituency Parsing by Pointing

• URL: http://arxiv.org/abs/2006.13557v1
• Date: Wed, 24 Jun 2020 08:29:09 GMT
• Title: Efficient Constituency Parsing by Pointing
• Authors: Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li
• Abstract summary: We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Our model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference.
• Score: 21.395573911155495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-the-art while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.

Related papers

• Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers [7.558415495951758]
  We propose a novel method for extracting parse trees from masked language models (LMs) Our method computes a score for each span based on the distortion of contextual representations resulting from linguistic perturbations. Our method consistently outperforms previous state-of-the-art methods on English with masked LMs, and also demonstrates superior performance in a multilingual setting.
  arXiv  Detail & Related papers  (2023-06-01T13:10:48Z)
• Boosting classification reliability of NLP transformer models in the long run [0.0]
  This paper compares different approaches to fine-tune a BERT model for a long-running classification task. Our corpus contains over 8 million comments on COVID-19 vaccination in Hungary posted between September 2020 and December 2021.
  arXiv  Detail & Related papers  (2023-02-20T14:46:54Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization [94.4409074435894]
  We propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR) Specifically, we propose to inject the standard Gaussian noise and regularize hidden representations of the fine-tuned model. We demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART.
  arXiv  Detail & Related papers  (2022-06-12T04:42:49Z)
• Unsupervised and Few-shot Parsing from Pretrained Language Models [56.33247845224995]
  We propose an Unsupervised constituent Parsing model that calculates an Out Association score solely based on the self-attention weight matrix learned in a pretrained language model. We extend the unsupervised models to few-shot parsing models that use a few annotated trees to learn better linear projection matrices for parsing. Our few-shot parsing model FPIO trained with only 20 annotated trees outperforms a previous few-shot parsing method trained with 50 annotated trees.
  arXiv  Detail & Related papers  (2022-06-10T10:29:15Z)
• Unifying Language Learning Paradigms [96.35981503087567]
  We present a unified framework for pre-training models that are universally effective across datasets and setups. We show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
  arXiv  Detail & Related papers  (2022-05-10T19:32:20Z)
• Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency [62.0887259003594]
  This work investigates three aspects of structured pruning on multilingual pre-trained language models: settings, algorithms, and efficiency. Experiments on nine downstream tasks show several counter-intuitive phenomena. We present Dynamic Sparsification, a simple approach that allows training the model once and adapting to different model sizes at inference.
  arXiv  Detail & Related papers  (2022-04-06T06:29:52Z)
• Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference [10.536415845097661]
  We propose a method for predicting the performance of NLI models without fine-tuning them. We show that the accuracy of the cosine similarity approach correlates strongly with the accuracy of the classification approach with a Pearson correlation coefficient of 0.65. Our method can lead to significant time savings in the process of model selection.
  arXiv  Detail & Related papers  (2022-02-21T18:10:24Z)
• An Application of Pseudo-Log-Likelihoods to Natural Language Scoring [5.382454613390483]
  A language model with relatively few parameters and training steps can outperform it on a recent large data set. We produce some absolute state-of-the-art results for common sense reasoning in binary choice tasks. We argue that robustness of the smaller model ought to be understood in terms of compositionality.
  arXiv  Detail & Related papers  (2022-01-23T22:00:54Z)
• Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning [74.25168207651376]
  Fine-tuning pre-trained language models to downstream cross-lingual tasks has shown promising results. We leverage continual learning to preserve the cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks. Our methods achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
  arXiv  Detail & Related papers  (2020-04-29T14:07:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.