Papers with DIORA
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (N19-1)
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| Challenge: | Using the deep inside-outside recursive autoencoder, we can extract both shallow parses and full syntactic trees from any domain or language automatically. |
| Approach: | They propose a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. |
| Outcome: | The proposed method outperforms previous methods on the WSJ dataset. |
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders (D19-1)
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| Challenge: | Existing models that use ground-truth part-of-speech tags are not always available and have significant weaknesses. |
| Approach: | They propose to use deep inside-outside recursive autoencoders to cluster the learned phrase vectors to induce span labels. |
| Outcome: | The proposed model outperforms ELMo and BERT on two versions of the Wall Street Journal dataset and improves over a previous state-of-the-art system that requires additional human annotations by 5 absolute F1 points (19% relative error reduction). |
Word Segmentation as Unsupervised Constituency Parsing (2022.acl-long)
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| Challenge: | Existing theories of word identification from continuous inputs are based on statistical cues, such as Bayesian inference and normative statistics. |
| Approach: | They propose a model which allows for a process isomorphic to unsupervised constituency parsing and which can reproduce human behavior in word identification experiments. |
| Outcome: | The proposed model reproduces human behavior in word identification experiments, suggesting it is viable to study word identification and its relation to syntactic processing. |
Deep Inside-outside Recursive Autoencoder with All-span Objective (2020.coling-main)
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| Challenge: | Existing neural approaches for constituency parsing are limited for low-resource languages and domains. |
| Approach: | They extend the training objective of DIORA by making use of all spans instead of only leaf-level spans. |
| Outcome: | The proposed model improves on two languages and provides better parsing accuracy than the original model. |
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders (2020.emnlp-main)
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| Challenge: | Syntactic parse trees are valuable intermediate features for many NLP pipelines. |
| Approach: | They propose an improved version of DIORA that encodes a single tree rather than a softly-weighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart. |
| Outcome: | The proposed model improves state-of-the-art in constituency parsing on the English WSJ Penn Treebank by 2.2-6% F1, depending on the data used for fine-tuning. |
Improved Latent Tree Induction with Distant Supervision via Span Constraints (2021.emnlp-main)
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Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O’Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum
| Challenge: | Distant supervision is not a practical way to perform unsupervised syntactic parsing. |
| Approach: | They propose a technique that uses distant supervision to improve unsupervised constituency parsing by using phrase bracketing. |
| Outcome: | The proposed method improves constituency parsing on English WSJ Penn Treebank by more than 5 F1 compared with full parse tree annotations. |