Papers with retrieval baselines
Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks (2023.eacl-main)
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| Challenge: | Statutory article retrieval (SAR) is a promising application of legal text processing. |
| Approach: | They propose a graph-augmented dense statute retriever model that incorporates the structure of legislation via a neural network to improve density retrieval performance. |
| Outcome: | The proposed model outperforms baselines on a real-world expert-annotated dataset. |
Semantic Parsing for Technical Support Questions (C18-1)
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| Challenge: | Automating technical support is a task of providing answers to complex problems . traditional approaches to this task rely on information retrieval and are keyword based . |
| Approach: | They propose a semantic parsing approach that uses grammatical structure to extract technical questions' attributes as a baseline and a CRF-based model that can improve performance in the presence of annotated data. |
| Outcome: | The proposed model outperforms retrieval baselines in annotated data for training. |
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets (2024.naacl-long)
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Hossein Aboutalebi, Hwanjun Song, Yusheng Xie, Arshit Gupta, Lijia Sun, Hang Su, Igor Shalyminov, Nikolaos Pappas, Siffi Singh, Saab Mansour
| Challenge: | Existing approaches to augment textual dialogues with retrieved images pose privacy, diversity, and quality constraints. |
| Approach: | They propose a framework to augment text-only dialogues with diverse and high-quality images by using a diffusion model and a feedback loop. |
| Outcome: | The proposed framework is comparable to or better than baselines, with significant improvements in human evaluation, especially against retrieval baselines where the image database is small. |
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset (2023.emnlp-main)
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| Challenge: | Existing models for named entity recognition have limited range when applied to long documents . lack of supervision for such a task means one has to settle for unsupervised approaches. |
| Approach: | They propose to train a neural context retrieval model based on an instruction-tuned large language model. |
| Outcome: | The proposed method outperforms baselines on an English literary dataset . pre-trained transformer-based models can perform named entity recognition (NER) with great accuracy, but limited range when applied to long documents such as whole novels. |