Papers by Torsten Scholak
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval (2025.emnlp-industry)
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Ahmed Masry, Megh Thakkar, Patrice Bechard, Sathwik Tejaswi Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte Suresh, Srivatsava Daruru, Enamul Hoque, Spandana Gella, Torsten Scholak, Sai Rajeswar
| Challenge: | Existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval. |
| Approach: | They propose a document retrieval model that bridges the gap between multimodal representation learning and document retrievals by providing external knowledge as context. |
| Outcome: | The proposed model achieves 3.61% improvement over existing retrieval models on the ViDoRe V2 benchmark, showing stronger generalization to out-of-domain benchmarks. |
DuoRAT: Towards Simpler Text-to-SQL Models (2021.naacl-main)
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| Challenge: | Recent text-to-SQL models can translate natural language questions to corresponding SQL queries on unseen databases. |
| Approach: | They propose a re-implementation of the RAT-SQL model that uses only relation-aware or vanilla transformers as the building blocks. |
| Outcome: | The proposed model is based on the spider dataset and shows it can be used on large databases without human intervention. |
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately . |
| Approach: | They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes . |
| Outcome: | The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show . |
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models (2021.emnlp-main)
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| Challenge: | Large pre-trained language models for textual data have an unconstrained output space . when fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. |
| Approach: | They propose a method for constraining auto-regressive decoders of language models through incremental parsing. |
| Outcome: | The proposed method can find valid output sequences by rejecting inadmissible tokens . it can be used on Spider and CoSQL text-to-SQl translation tasks . |