Papers by Ruben Janssen
Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering (2024.lrec-main)
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Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro Szekely, Alessandro Moschitti
| Challenge: | Existing approaches to QA over textual data are based on a "retrieve-then-generate" pipeline. |
| Approach: | They propose a "triple-level" labeling strategy that infers fine-grained labels and trains a re-ranker to improve relevance of retrieved triples. |
| Outcome: | The proposed pipeline improves on prior KGQA systems by 5.56% Exact Match. |
Speechworthy Instruction-tuned Language Models (2024.emnlp-main)
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Hyundong Cho, Nicolaas Jedema, Leonardo Ribeiro, Karishma Sharma, Pedro Szekely, Alessandro Moschitti, Ruben Janssen, Jonathan May
| Challenge: | Current instruction tuned language models are trained on textual preference data and therefore not aligned to speech domain. |
| Approach: | They propose to use radio-industry best practices to prompt and learn speech-based preference data to improve speech-suitability of popular instruction tuned language models. |
| Outcome: | The proposed methods achieve the best win rates in head-to-head comparisons, resulting in preferred or tied to the base model in 76.2% of comparisons on average. |