Papers with sequence modeling
Long-range Sequence Modeling with Predictable Sparse Attention (2022.acl-long)
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| Challenge: | Existing approaches to capture global context dependencies in sequence modeling suffer from quadratic complexity in time and memory usage. |
| Approach: | They propose an efficient Transformer architecture for fast long-range sequence modeling with a sparse attention matrix and a hidden state cross module. |
| Outcome: | The proposed architecture outperforms the standard multi-head attention and its variants in various long-sequence tasks with low computational costs. |
Memformer: A Memory-Augmented Transformer for Sequence Modeling (2022.findings-aacl)
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| Challenge: | Experimental results show that Memformer uses 8.1x less memory space and 3.2x faster on inference. |
| Approach: | They propose an efficient neural network that utilizes an external dynamic memory to encode and retrieve past information. |
| Outcome: | The proposed model achieves comparable performance against baselines with 8.1x less memory space and 3.2x faster on inference. |
Token Pruning for Improving Graph-Generating State Space Model Performance (2026.eacl-srw)
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| Challenge: | State Space Models (SSMs) are efficient alternatives to Transformers for sequence modeling, but extending them to two-dimensional vision tasks remains challenging. |
| Approach: | They propose a leaf-guided pruning strategy that accelerates GG-SSM inference . they selectively scales or bypasses secondary refinement computations associated with leaf nodes . |
| Outcome: | The proposed pruning strategy accelerates GG-SSM inference without modifying graph topology . the proposed pruning method achieves throughput improvements with controlled accuracy degradation . |
lrnnx: A library for Linear RNNs (2026.eacl-srw)
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Karan Bania, Soham Kalburgi, Manit Tanwar, null Dhruthi, Aditya Nagarsekar, Harshvardhan Mestha, Naman Chibber, Raj Deshmukh, Anish Sathyanarayanan, Aarush Rathore, Pratham Chheda
| Challenge: | Existing implementations of linear recurrent neural networks are fragmented across different software frameworks . existing implementations often require custom CUDA kernels or lack publicly available code altogether . |
| Approach: | a unified software library implements several modern LRNN architectures under a common interface. lrnnx aims to improve accessibility, reproducibility, and extensibility of LRnn research and applications. |
| Outcome: | lrnnx aims to improve accessibility, reproducibility, and extensibility of LRNN research and applications. |
Scaling Hidden Markov Language Models (2020.emnlp-main)
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| Challenge: | Hidden Markov models are a fundamental tool for sequence modeling that separates the hidden state from the emission structure. |
| Approach: | They propose methods for scaling hidden Markov models to massive state spaces while maintaining efficient exact inference and effective regularization. |
| Outcome: | The proposed methods are much more accurate than previous HMMs and n-gram-based methods, making progress towards the performance of state-of-the-art NN models. |
Efficient Convolutional Neural Networks for Diacritic Restoration (D19-1)
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| Challenge: | Diacritic restoration is a computational task that requires a computer to understand written texts. |
| Approach: | They propose to use Temporal Convolutional Neural Networks (TCN) to restore missing diacritics for each character in written text. |
| Outcome: | The proposed model improves on TCN in Arabic, Yoruba, and Vietnamese. |
Native Hybrid Attention for Efficient Sequence Modeling (2026.acl-long)
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| Challenge: | Experimental results show that NHA surpasses Transformers on recall-intensive tasks. |
| Approach: | They propose a hybrid architecture of linear and full attention that integrates both into a unified layer design. |
| Outcome: | The proposed architecture surpasses Transformers and other hybrid baselines on recall-intensive and commonsense reasoning tasks. |
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog (N18-1)
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| Challenge: | Existing approaches to reading comprehension on multiparty dialogs have focused on children's stories or newswire. |
| Approach: | They propose a new corpus and a robust deep learning architecture for a task in reading comprehension on multiparty dialog. |
| Outcome: | The proposed model outperforms the state-of-the-art model on a different genre using bidirectional LSTM, showing a 13.0+% improvement for longer dialogs. |
Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models (2025.naacl-long)
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| Challenge: | Large pre-trained models have achieved outstanding results in sequence modeling . alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address these inefficiencies. |
| Approach: | They propose to reduce the size and computational overhead of large pre-trained models by removing selected components at different granularities. |
| Outcome: | The proposed models achieve a speedup of up to 1.4x during inference while maintaining accuracy. |
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling (2022.acl-long)
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| Challenge: | Using attention-based models, certain tokens are less ambiguous than others, and they require fewer refinements for disambiguation. |
| Approach: | They propose a lazy transition mechanism to adjust the significance of iterative refinements for each token representation. |
| Outcome: | The proposed model outperforms baseline models on several tasks with the same number of parameters. |
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning (P18-1)
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| Challenge: | Practical applications of automatic image description systems include leveraging descriptions for image indexing or retrieval, and helping those with visual impairments by transforming visual signals into information that can be communicated via text-to-speech technology. |
| Approach: | They propose to extract and filter image caption annotations from billions of webpages and use them to train models. |
| Outcome: | The proposed model architectures perform better when trained on the Conceptual Captions dataset. |
Assessing the Ability of Self-Attention Networks to Learn Word Order (P19-1)
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| Challenge: | Existing studies have attributed SAN to being weak at learning positional information for sequence modeling due to lack of recurrence structure. |
| Approach: | They propose a word reordering detection task to quantify how well word order information is learned by SAN and RNN. |
| Outcome: | The proposed task quantifies how well word order information learned by SAN and RNN is learned. |
When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute (2021.emnlp-main)
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| Challenge: | Large language models are difficult to train because of the growing computation time and cost. |
| Approach: | They propose a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. |
| Outcome: | The proposed model achieves state-of-the-art on a Wiki-103 and Billion Word datasets using 1.6 days of training on an 8-GPU machine. |
Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM (L18-1)
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| Challenge: | Part-of-speech (POS) tagging is one of the most important building blocks in many natural language processing (NLP) applications. |
| Approach: | They propose to use a POS tagger for Arabic Gulf dialect to improve POS tagging accuracy. |
| Outcome: | The proposed POS tagger improves POS tagging accuracy for the Arabic Gulf dialect from 75% accuracy to 91% accuracy using a bi-LSTM labeler. |
Exploring the Limitations of Mamba in COPY and CoT Reasoning (2025.emnlp-main)
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| Challenge: | Inference overhead of Transformers increases linearly with the sequence length, posing challenges for modeling long sequences. |
| Approach: | They analyze Mamba's expressive ability to perform COPY operations and Chain of Thought reasoning tasks using a defined sequence length. |
| Outcome: | The proposed model can perform COPY operations and Chain of Thought reasoning tasks with a constant size while reducing computational costs. |
Lattice Transformer for Speech Translation (P19-1)
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| Challenge: | Recent advances in sequence modeling have highlighted the strengths of the transformer architecture. |
| Approach: | They propose a general lattice transformer for speech translation where the input is the output of the automatic speech recognition (ASR) they propose 'controllable' lattica attention mechanism to consume latent representations. |
| Outcome: | The proposed model outperforms baseline and lattice LSTM on the Chinese-English translation task. |
Emergence of Abstract State Representations in Embodied Sequence Modeling (2023.emnlp-main)
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| Challenge: | Sequence modeling is a simple yet versatile task that can be applied to more complex decision-making domains. |
| Approach: | They build a sequence modeling Transformer which takes a language instruction, actions, and environmental observations as inputs and then trains a model to reconstruct environmental layouts. |
| Outcome: | The proposed model can reconstruct environmental layouts from the inputs of the model and language instructions play a role in the reconstruction accuracy. |
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language Modeling (2024.emnlp-main)
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| Challenge: | a task-agnostic visual encoding yields minimal performance gains on grounding, but Transformers outperform Mamba at in-context multimodal retrieval. |
| Approach: | They propose to replace Transformers in Visual Language Models with Mamba, a structured state space model that demonstrates promising performance in sequence modeling. |
| Outcome: | The proposed model outperforms Transformers-based models in captioning, question answering, and reading comprehension. |
RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals (2025.findings-emnlp)
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| Challenge: | Transformers have shown strong performance in processing sequential data, but their parameters are larger . a novel approach to reduce the model parameters while maintaining high performance is proposed . |
| Approach: | They propose a transformer-based model that processes input repeatedly in a circular, ring-like manner. |
| Outcome: | The proposed approach reduces model parameters while maintaining high performance . the proposed approach is validated in the experiments. |