Papers by Gonçalo Simões
Text Segmentation by Cross Segment Attention (2020.emnlp-main)
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| Challenge: | Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents. |
| Approach: | They propose three transformer-based NLP models that break up text into constituents and compare them to previous approaches. |
| Outcome: | The proposed architectures reduce errors by a large margin on three datasets and improve performance on real-world datasets. |
Planning with Learned Entity Prompts for Abstractive Summarization (2021.tacl-1)
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| Challenge: | a simple but flexible mechanism is used to ground the generation of abstractive summaries. |
| Approach: | They propose a mechanism to learn an intermediate plan to ground the generation of abstractive summaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods for faithfulness on CNN and BillSum. |
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings (P18-1)
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| Challenge: | recurrent neural networks have produced significant advances in part-of-speech tagging accuracy . a common feature of these models is the presence of rich initial word encodings . however, word or sub-word information interacts only through subsequent recursive layers . |
| Approach: | They propose to use recurrent neural networks with sentence-level context for initial character and word-based representations. |
| Outcome: | The proposed model has the highest accuracy of all participating systems in the CoNLL 2017 task. |
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation (2022.acl-long)
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Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, Mirella Lapata
| Challenge: | Composition Sampling is a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. |
| Approach: | They propose a method to generate diverse outputs for conditional generation . they use a plan-based neural generation model that is trained to create a composition of the output and then generate by conditioning on it and the input. |
| Outcome: | The proposed method avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain. |
Data Augmentation for Low-Resource Dialogue Summarization (2022.findings-naacl)
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| Challenge: | DADS generates synthetic examples by replacing sections of text from input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the simulated dialogue. |
| Approach: | They propose a Data Augmentation technique for low-resource Dialogue Summarization that uses pretrained language models to generate diverse alternatives. |
| Outcome: | The proposed method generates synthetic examples from a low-resource dataset . it produces topically diverse examples without introducing additional hallucinations . |