Papers by Nadi Tomeh
Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
ToMMeR - Efficient Entity Mention Detection from Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods to detect text spans that refer to entities are often conflated with entity typing in a single joint task. |
| Approach: | They propose a lightweight model that probes mention detection capabilities from early LLM layers. |
| Outcome: | The proposed model achieves 93% recall zero-shot with 90% precision under human-calibrated LLM-judge protocol . |
Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement (2025.naacl-short)
Copied to clipboard
| 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. |
Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations (2020.coling-main)
Copied to clipboard
| Challenge: | Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function. |
| Approach: | They propose to use Arabic dependency annotations to parse projective dependency trees using CATiB and UD treebanks. |
| Outcome: | The proposed model gives 9.9% error reduction on CATiB and 6.1% on UD compared to a strong baseline and ablation tests show that the main contribution is given by sharing tree representation between tasks, and not simply sharing biLSTM layers as is often performed in NLP multitask systems. |
GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks (2022.acl-srw)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
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. |