Papers with pointer networks
Hierarchical Pointer Net Parsing (D19-1)
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| Challenge: | Existing approaches to parsing are greedy transition-based and globally optimized . however, the decision-making process is based on local information, causing error propagation to subsequent steps. |
| Approach: | They propose hierarchical pointer network parsers and apply them to dependency and sentence-level discourse parsing tasks. |
| Outcome: | The proposed method outperforms existing methods and sets new state-of-the-art methods on benchmark datasets. |
Stack-Pointer Networks for Dependency Parsing (P18-1)
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| Challenge: | Existing approaches to dependency parsing are local and greedy transitionbased . StackPtr parsers use the information of whole sentences and previously derived subtree structures . |
| Approach: | They propose a stack-pointer network-based dependency parser that reads whole sentence and builds dependency tree top-down in a depth-first fashion. |
| Outcome: | The proposed model reads and encodes whole sentence, then builds dependency tree top-down (from root-to-leaf) in a depth-first fashion. |
Towards Domain-Independent Text Structuring Trainable on Large Discourse Treebanks (2020.findings-emnlp)
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| Challenge: | Text structuring is a fundamental step in natural language generation, especially when generating multi-sentential text. |
| Approach: | They propose a novel task that combines neural dependency tree induction with pointer networks to train on large discourse treebanks. |
| Outcome: | The proposed method outperforms existing content ordering metrics and outperformed existing ones. |
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan
| Challenge: | Fact Extraction and Verification datasets provide a resource for end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. |
| Approach: | They propose a system that is resilient to attacks by multiple propositions, temporal reasoning, ambiguity and lexical variation and a sequence of evidence sentences and veracity relation predictions. |
| Outcome: | The proposed system is resilient to three realistic “attacks” and obtains state-of-the-art results due to improved evidence retrieval. |
Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond (2023.findings-acl)
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| Challenge: | Existing solutions for word probability distributions are limited and the output softmax layer is inherently limited. |
| Approach: | They propose to use the output softmax layer to compute the word probability distribution instead of using pointer networks to break the bottleneck. |
| Outcome: | The proposed method improves factCC score by 2 points in CNN/DM and XSUM dataset, and MAUVE scores by 30% in bookSum paragraph-level dataset. |