Papers by Thierry Charnois
Higher-Order Dependency Parsing for Arc-Polynomial Score Functions via Gradient-Based Methods and Genetic Algorithm (2022.aacl-main)
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| Challenge: | Existing methods for higher-order dependency parsing are based on arc-polynomials . a score function is linear in arc variables, while for second-order models it is linear . |
| Approach: | They propose a method which takes advantage of arc-polynomials score functions . they use coordinate ascent and genetic search to iteratively update a candidate parse . |
| Outcome: | The proposed method matches state-of-the-art second order parsers on three standard datasets. |
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. |
GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer (2024.naacl-long)
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| Challenge: | Named Entity Recognition (NER) models are limited to a set of predefined entity types. Large language models (LLMs) can extract arbitrary entities through natural language instructions. |
| Approach: | They propose a model that can identify any type of entity using a transformer encoder. |
| Outcome: | The proposed model outperforms existing models on NER benchmarks on a set of predefined entities. |
Leveraging Text-to-Text Transformers as Classifier Chain for Few-Shot Multi-Label Classification (2025.emnlp-main)
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| Challenge: | Multilabel text classification (MLTC) is an essential task in NLP applications. |
| Approach: | They propose a distillation-based T5 generalist model for zero-shot MLTC and few-shot fine-tuning. |
| Outcome: | The proposed model outperforms baselines of similar size on three few-shot tasks. |
Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement (2025.naacl-short)
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| Challenge: | Existing graph-based dependency parsers use a standard two-pipeline approach that only scores arcs and labels . |
| Approach: | They propose a graph-based dependency parsing architecture that explicitly constructs vectors from which both arcs and labels are scored. |
| Outcome: | The proposed model outperforms state-of-the-art models on PTB and UD in accuracy and efficiency. |
GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks (2022.acl-srw)
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| Challenge: | Named Entity Recognition (NER) uses sequence labelling and span classification to identify entities. |
| Approach: | They propose a framework that uses Graph Neural Networks to enrich the span representation to reduce the number of overlapping spans during prediction. |
| Outcome: | The proposed framework reduces the number of overlapping spans while maintaining competitive metric performance. |
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. |
Enhancing Few-Shot Topic Classification with Verbalizers. a Study on Automatic Verbalizer and Ensemble Methods (2024.lrec-main)
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Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah, Thierry Charnois, Hanene Azzag, Santiago Cordoba Muñoz
| Challenge: | Pretrained language models are increasingly being used for many tasks. |
| Approach: | They propose to use verbalizers to help interpret masked word distributions into output predictions. |
| Outcome: | The proposed approach outperforms models trained with individual templates while using significantly less resources. |