Papers with CCG supertagging
Holographic CCG Parsing (2023.acl-long)
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| Challenge: | Existing methods for supertagging and parsing use black-box neural architectures to implicitly model phrase structure dependencies. |
| Approach: | They propose a method for formulating CCG as a recursive composition in a continuous vector space by using holographic embeddings as holography operator. |
| Outcome: | The proposed method can achieve comparable performance to state-of-the-art parsing with Transformers. |
Targeted Syntactic Evaluation of Language Models (D18-1)
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| Challenge: | Recent advances have led to an explosion of neural network-based LM architectures. |
| Approach: | They propose to supplement perplexity with a metric that assesses whether a language model can predict the grammatical sentence more accurately than an ungrammatically-based model. |
| Outcome: | The proposed model performed poorly on many of the constructions. |
An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search (N19-1)
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| Challenge: | Neural encoder-decoder models have been successful at a variety of NLP tasks, including machine translation, parsing, and dialog generation. |
| Approach: | They propose a method for search-aware training via a continuous relaxation of beam search to enable global normalization. |
| Outcome: | The proposed approach is able to train globally normalized recurrent sequence models through simple backpropagation. |
Hierarchically-Refined Label Attention Network for Sequence Labeling (D19-1)
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| Challenge: | Conditional random fields (CRF) is a powerful model for statistical sequence labeling, but it does not give much information gain over strong neural encoding. |
| Approach: | They propose a hierarchically-refined label attention network which captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. |
| Outcome: | The proposed model improves POS tagging accuracy and speeds up training and testing compared to the current model. |
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks (2020.emnlp-main)
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| Challenge: | Existing studies have focused on supertagging but have not tapped into contextual information. |
| Approach: | They propose to build a graph from chunks extracted from a lexicon and apply attention over it to enhance supertagging by leveraging contextual information. |
| Outcome: | The proposed approach outperforms previous studies in terms of supertagging and parsing. |