Papers by Grigorii Guz
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. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
Neural RST-based Evaluation of Discourse Coherence (2020.aacl-main)
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| Challenge: | Existing discourse parsers cannot predict coherent texts without using silver-standard features. |
| Approach: | They propose a tree-recursive neural model which takes advantage of the text’s RST features produced by a state of the art RST parser and compares it to the current state of art. |
| Outcome: | The proposed model achieves state-of-the-art accuracy on the Grammarly Corpus for Discourse Coherence (GCDC) and has 62% fewer parameters than existing models. |