Papers by Torsten Scholak

4 papers
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval (2025.emnlp-industry)

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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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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 .

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