Papers with neural modeling
Tractable & Coherent Multi-Document Summarization: Discrete Optimization of Multiple Neural Modeling Streams via Integer Linear Programming (2022.emnlp-industry)
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| Challenge: | Multi-document summarization generates summary of corpus of documents consisting of related topics. |
| Approach: | They propose a generic framework to jointly consider coherence and informativeness in multi-document summarization and offers provisions to replace individual components based on the domain of source text. |
| Outcome: | The proposed framework consistently performs better than baselines for objective metrics and human evaluation. |
Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining (2023.eacl-main)
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| Challenge: | Existing studies on robustness of pretrained multilingual models are limited to the English language. |
| Approach: | They propose to use data augmentation and contrastive loss term to boost robustness of multilingual models in cross-lingual settings. |
| Outcome: | The proposed model outperforms existing models on clean and noisy data in the cross-lingual setting. |
Scaling Hidden Markov Language Models (2020.emnlp-main)
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| Challenge: | Hidden Markov models are a fundamental tool for sequence modeling that separates the hidden state from the emission structure. |
| Approach: | They propose methods for scaling hidden Markov models to massive state spaces while maintaining efficient exact inference and effective regularization. |
| Outcome: | The proposed methods are much more accurate than previous HMMs and n-gram-based methods, making progress towards the performance of state-of-the-art NN models. |