Papers with SLR
SLR: Automated Synthesis for Scalable Logical Reasoning (2026.acl-long)
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Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia Wüst, Hikaru Shindo, Rupert Mitchell, Tim Woydt, Patrick Schramowski, Wolfgang Stammer, Kristian Kersting
| Challenge: | Existing benchmarks intended to evaluate reasoning capabilities emphasize deductive reasoning, where conclusions necessarily follow from given premises. |
| Approach: | They propose an end-to-end framework for systematic evaluation and training of Large Language Models via Scalable Logical Reasoning. |
| Outcome: | The proposed framework doubles Llama-3-8B accuracy on SLR-Bench, achieving parity with Gemini-Flash-Thinking at a fraction of computational cost. |
Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) have made significant progress on different language tasks, but they tend to "hallucinate" plausible but factually incorrect answers. |
| Approach: | They propose to integrate knowledge graphs (KGs) into LLM inference to reduce hallucinations by searching online and applying a selection process. |
| Outcome: | The proposed integration improves performance on benchmark datasets and also to mitigate hallucinations. |
A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News (2024.lrec-main)
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| Challenge: | a new dataset is being developed to enrich resources for sign language research . the dataset is 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words. |
| Approach: | They introduce a new Hong Kong sign language dataset called TVB-HKSL-News . the dataset is collected from a TV news program and contains sign videos . they aim to support research in sign language recognition and translation . |
| Outcome: | The proposed dataset supports sign language recognition and translation research in Hong Kong . it consists of 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words . |
LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature Reviews (2025.findings-acl)
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| Challenge: | Existing methods for abstract screening focus on binary classification settings; existing question answering (QA) based ranking approaches suffer from error propagation. |
| Approach: | They propose a systematic literature review (SLR) method that uses large language models to evaluate the SLR's inclusion and exclusion criteria. |
| Outcome: | The proposed method outperforms existing question answering (QA) based methods by 5-10 pp. in mean precision. |
Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition (2024.lrec-main)
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| Challenge: | Current sign language recognition methods use spatial graphs and temporal modules to capture spatial and temporal features, but their spatial graph modules are typically built on fixed graph structures. |
| Approach: | They propose a new spatial architecture that captures input-sensitive joint relationships and a temporal module to model multi-scale temporal information to capture complex human dynamics. |
| Outcome: | The proposed method achieves state-of-the-art accuracy on four large-scale SLR benchmarks. |
Better Sign Language Translation with STMC-Transformer (2020.coling-main)
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| Challenge: | Current SLT approaches use a sign language recognition system to extract sign language glosses from videos. |
| Approach: | They propose to use a Sign Language Recognition system to extract sign language glosses from videos and a translation system to generate spoken language translations from the glossed sign language. |
| Outcome: | The proposed system outperforms existing methods on gloss-to-text and video-to text translations on the ASLG-PC12 corpus. |
SMILE Swiss German Sign Language Dataset (L18-1)
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Sarah Ebling, Necati Cihan Camgöz, Penny Boyes Braem, Katja Tissi, Sandra Sidler-Miserez, Stephanie Stoll, Simon Hadfield, Tobias Haug, Richard Bowden, Sandrine Tornay, Marzieh Razavi, Mathew Magimai-Doss
| Challenge: | The goal of an ongoing three-year project in Switzerland is to pioneer an assessment system for lexical signs of Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) that relies on sign language recognition. |
| Approach: | The goal of the project is to pioneer an assessment system for lexical signs of Swiss German Sign Language that relies on sign language recognition. |
| Outcome: | The system will give adult L2 learners of DSGS feedback on the correctness of the manual parameters (handshape, hand position, location, and movement) of isolated signs they produce. |
The Key Points: Using Feature Importance to Identify Shortcomings in Sign Language Recognition Models (2024.lrec-main)
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| Challenge: | Pose estimation keypoints are widely used in sign language recognition (SLR) but they are difficult to achieve due to the large degree of variability between occurrences of the same sign, the lack of large datasets and the imbalanced nature of the data. |
| Approach: | They propose to use pose estimation keypoints to generalise to unseen signers by identifying potentially redundant features and identifying key points that are most informative to SLR . they propose to train models with large datasets and labelled data to find key points which are redundant to differentiating between signs . |
| Outcome: | The proposed model can be trained on large datasets and has more generalised features than would be possible with a small dataset. |