Papers with SLP
Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production (2026.acl-long)
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| Challenge: | Existing approaches to sign language production use autoregressive or diffusion models that generate one-by-one output tokens but suffer from exposure bias during inference. |
| Approach: | They propose a hybrid autoregressive-diffusion model that combines iterative refinement and sequential dependency modeling for Sign Language production. |
| Outcome: | The proposed model improves sign language production quality and real-time efficiency on PHOENIX14T and How2Sign. |
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language (2022.acl-short)
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| Challenge: | Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community. |
| Approach: | They leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties. |
| Outcome: | The proposed model outperforms existing approaches on signs unobserved during training. |
Splits! Flexible Sociocultural Linguistic Investigation at Scale (2026.acl-long)
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| Challenge: | Variation in language use offers a rich lens into cultural perspectives, values, and opinions. |
| Approach: | They propose to construct a "sandbox" for systematic and flexible sociolinguistic research by splitting a reddit dataset into demographically/topically split SLPs. |
| Outcome: | The proposed method analyzes a demographically/topically split Reddit dataset validated by self-identification and replicating several known SLPs from existing literature. |
Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words (2022.lrec-1)
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| Challenge: | Existing vision-based sign language production approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems. |
| Approach: | They propose an avatar-based sign language production system that generates sign language videos from spoken language expressions. |
| Outcome: | The proposed system achieves higher BLEU-4 and higher ROUGE-L scores on a new Korean-Korean sign language dataset. |
T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text (2024.acl-long)
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| Challenge: | Existing vector quantization methods are fixed-length encodings, overlooking the uneven information density in sign language. |
| Approach: | They propose a two-stage sign language production paradigm that encodes sign language sequences into discrete codes and autoregressively generates sign languages from text. |
| Outcome: | The proposed model can dynamically adjust the encoding length based on the information density in sign language to achieve accurate and compact encoded enccoding. |
SignAlignLM: Integrating Multimodal Sign Language Processing into Large Language Models (2025.findings-acl)
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| Challenge: | Deaf and Hard-of-Hearing (DHH) users increasingly utilize Large Language Models (LLMs), yet face significant challenges due to these models’ limited understanding of sign language grammar, multimodal sign inputs, and Deafic cultural contexts. |
| Approach: | They propose to use sign language support in LLMs to integrate sign linguistic rules and conventions into prompting and fine-tuning strategies to address the needs of DHH users. |
| Outcome: | The proposed model can be generalized interfaces for both spoken and signed languages if trained with a multitasking paradigm. |
Including Signed Languages in Natural Language Processing (2021.acl-long)
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| Challenge: | Existing research in Sign Language Processing (SLP) rarely explores signed languages . authors urge adoption of an efficient tokenization method and the collection of real-world signed language data . |
| Approach: | They propose to include signed languages as a research area with high social and scientific impact . they review the limitations of current SLP models and identify the open challenges . |
| Outcome: | The proposed model should include signed languages as a research area with high social and scientific impact. |
Stable Signer: Hierarchical Sign Language Generative Model (2026.acl-long)
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| Challenge: | Sign Language Production (SLP) is the process of converting complex input text into a real video. |
| Approach: | They propose a new sign language generative model that streamlines redundant structure and optimizes the task objective. |
| Outcome: | The proposed model streamlines redundant structure and optimizes objective . it generates high-quality and multi-style sign language videos with hand gestures . |
Dicta-Sign-LSF-v2: Remake of a Continuous French Sign Language Dialogue Corpus and a First Baseline for Automatic Sign Language Processing (2020.lrec-1)
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| Challenge: | Existing research on automatic Sign Language Processing (SLP) has focused on recognizing lexical signs, but other gestural units like iconic structures need to be recognized. |
| Approach: | They propose a public remake of the French Sign Language part of the Dicta-Sign corpus with clean annotations and a Convolutional-Recurrent Neural Network to train and test it. |
| Outcome: | The proposed version of the publicly available SL corpus Dicta-Sign is limited to its French Sign Language part and includes lexical and non-lexical annotations over 11 hours of video recording with 35000 manual units. |
Multi-Channel Spatio-Temporal Transformer for Sign Language Production (2024.lrec-main)
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| Challenge: | Sign language production models ignore structural correlations between channels and use multi-channel spatial attention to capture correlations across channels. |
| Approach: | They propose a novel approach to transform sign language into a unified feature representation using multi-channel spatial attention and temporal attention to learn sequential dependencies for each channel over time. |
| Outcome: | The proposed model outperforms state-of-the-art models on two sign language datasets from diverse cultures. |
Training-Free Adaptive Speculative Decoding via Linguistic Priors (2026.findings-acl)
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| Challenge: | Speculative decoding (SPD) is a promising technique to accelerate Large Language Models (LLMs). current approaches neglect the inherent heterogeneity of natural language and fail to distinguish between semantically-rich content and structurally-predictable syntax. |
| Approach: | They propose a training-free framework that leverages linguistic priors to enable adaptive drafting and verification. |
| Outcome: | The proposed framework significantly accelerates inference without additional training. |
TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024.lrec-main)
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Ziqiang Liu, Shujie Li, Zefeng Cai, Xiangyu Li, Yunshui Li, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Existing pre-training frameworks for text-to-SQL parsing have shown inherent differences in distributions between tables and plain text. |
| Approach: | They propose a framework to improve context-dependent Text-to-SQL parsing by leveraging Linking information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two leading downstream benchmarks. |
Can Small Vision–Language Models Perform Sign Language Translation? (2026.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) have shown strong generalization across multimodal tasks, but their capacity to handle sign language translation (SLT) remains unclear. |
| Approach: | They propose entity- and semantics-aware metrics tailored for SLT to evaluate their performance. |
| Outcome: | The proposed metrics highlight the limitations of general-purpose VLMs to SLT, unlike their applicability in other tasks. |