Papers by Niama El Khbir
Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)
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| Challenge: | Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding. |
| Approach: | They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search. |
| Outcome: | The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets. |
Filtered Semi-Markov CRF (2023.findings-emnlp)
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| Challenge: | Existing methods for sequence labeling tasks such as Named Entity Recognition (NER) suffer from quadratic complexity over sequence length and poor performance compared to CRF. |
| Approach: | They propose a variant of Semi-Markov CRF that incorporates a filtering step to eliminate irrelevant segments, reducing complexity and search space. |
| Outcome: | The proposed method outperforms both CRF and Semi-CRF on several NER benchmarks while being significantly faster. |