Papers with space
Modelling Narrative Elements in a Short Story: A Study on Annotation Schemes and Guidelines (2020.lrec-1)
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| Challenge: | Text-processing algorithms that annotate main components of a story are in great need of corpora and well-agreed annotation schemes. |
| Approach: | They propose a model that generalizes a narrative structure in the form of world building elements (characters, time and space) and text worlds themselves and switches between them. |
| Outcome: | The proposed model can be used for annotating narratives in corpora of literary texts, criminal evidence, teaching materials, quests, etc. |
Basreh or Basra? Geoparsing Historical Locations in the Svoboda Diaries (2024.acl-srw)
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| Challenge: | In the historical domain, many geoparsing corpora are from large news collections. |
| Approach: | They propose a pipeline employing named entity recognition for geotagging and a map-based generate-and-rank approach incorporating candidate name augmentation and clustering of location context words for geocoding. |
| Outcome: | The proposed pipeline outperforms existing map-based geoparsers in terms of accuracy, lowest mean distance error, and number of locations correctly identified. |
Time-and-Space-Efficient Weighted Deduction (2023.tacl-1)
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| Challenge: | Unweighted deduction allows a generic forward-chaining execution strategy, but weighted deduction requires a constant factor more time and space. |
| Approach: | They propose a generic unweighted deduction strategy that uses a factor more time and space than unweighting deduction . they also propose an extension to cyclic deduction systems based on Tarjan . |
| Outcome: | The proposed method is based on the proposed method and is compared with cyclic deduction systems. |
Efficient Classification of Long Documents Using Transformers (2022.acl-short)
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| Challenge: | Several methods have been proposed for classifying long textual documents using Transformers, but there is a lack of consensus on a benchmark to enable a fair comparison among different approaches. |
| Approach: | They propose to use a dataset to evaluate the relative efficacy of various models for long document classification using Transformers. |
| Outcome: | The proposed models outperform simple baseline models and yield inconsistent performance across datasets. |
Compact Personalized Models for Neural Machine Translation (D18-1)
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| Challenge: | a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality. |
| Approach: | They propose gradient-based domain adaptation methods for self-attentive machine translation models . they encourage structured sparsity in the set of offset tensors during learning . |
| Outcome: | The proposed method achieves high space and time efficiency using sparse models . the results compare the proposed method with incremental adaptation . |
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph (2022.coling-1)
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| Challenge: | Existing models that learn word embeddings rely on a large corpus of data . however, these models require massive time and space for data pre-processing and training . |
| Approach: | They propose a model that learns word embeddings utilizing only dictionaries and thesauri . they exploit a new context-focused loss model that models transitive relationships between word pairs . |
| Outcome: | The proposed model reaches the state-of-art on multiple word similarity and relatedness benchmarks. |
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model (2024.emnlp-main)
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| Challenge: | Existing approaches to fine tune LLMs produce unsafe responses and unreliable reasoning, but this solution introduces substantial time and space overhead due to the separate models required. |
| Approach: | They propose to insert extra parameters into transformer architecture to predict calibration signals along with original LLM output. |
| Outcome: | The proposed model reduces time and space costs while enabling seamless online deployment. |
On Knowledge distillation from complex networks for response prediction (N19-1)
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| Challenge: | Recent advances in Question Answering have led to the development of very complex models . however, these models are expensive in space and time and require limited resources . |
| Approach: | They propose to use simple models which learn to emulate characteristics of a teacher network . they use a 12GB Tesla K80 GPU to restrict the maximum length of the input document . |
| Outcome: | The proposed model can perform better on a Holl-E dialog dataset. |
Dodo: Dynamic Contextual Compression for Decoder-only LMs (2024.acl-long)
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| Challenge: | Existing approaches to NLP are sparsifying attention patterns or approximating the attention computation with kernel methods. |
| Approach: | They propose a method for dynamic contextual compression for decoder-only LMs. |
| Outcome: | The proposed method reduces the cost of self-attention to a fraction of typical time and space. |
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator (2023.findings-acl)
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| Challenge: | Existing transformer models are computationally demanding and prohibitively costly for long sequences due to the quadratic complexity of its selfattention module. |
| Approach: | They propose a transformer-based model that inherits weights from large pretrained models by removing redundancies in hidden sequences using the ready-made Fast Fourier Transform operator. |
| Outcome: | The proposed model outperforms the standard BART model on the long-range modeling benchmark LRA with significant improvements in speed and space. |
A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior (2025.acl-long)
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| Challenge: | Standard models that focus on fixation durations ignore spatial dynamics of reading . authors propose a model that captures how long fixations last, where they land and when . |
| Approach: | They propose a generative model that captures how long fixations last and where they land and when they occur. |
| Outcome: | The proposed model exhibits higher likelihood on held-out reading data than baselines. |
Injecting Context via Situation Working Memory for Logical Reasoning with LLMs (2026.acl-long)
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| Challenge: | Recent advances in large language models have improved logical reasoning by injecting formal logic or explicit structured representations. |
| Approach: | a cognitively inspired method is proposed to help LLMs construct a mental representation of events . SituW builds a situation representation by decomposing text along these five dimensions . it also guides LLM inference with this evolving state . |
| Outcome: | a cognitively inspired method improves accuracy and predictability in large language models . SituW builds a mental representation by decomposing text along these dimensions . |