Papers with self-attention layer
Attention over Heads: A Multi-Hop Attention for Neural Machine Translation (P19-2)
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| Challenge: | Existing multihop attentions for machine comprehension are recurrent and hierarchical . a proposed multi-hop attention for the Transformer refines the attention for an output symbol many times . |
| Approach: | They propose a multi-hop attention for the Transformer which integrates attentions from each head. |
| Outcome: | The proposed model outperforms the baseline Transformer in terms of translation accuracy and speed. |
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks (2022.emnlp-main)
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| Challenge: | Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whether neural models exhibit compositional behaviour. |
| Approach: | They propose a transformer-based model that outperforms specialized architectures on ReaSCAN and a modified version of gSCAN to test their performance. |
| Outcome: | The proposed model outperforms specialized architectures on ReaSCAN and gSCAN on a grid world and can generalize to deeper input structures. |
DocTime: A Document-level Temporal Dependency Graph Parser (2022.naacl-main)
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Puneet Mathur, Vlad Morariu, Verena Kaynig-Fittkau, Jiuxiang Gu, Franck Dernoncourt, Quan Tran, Ani Nenkova, Dinesh Manocha, Rajiv Jain
| Challenge: | Document dependency graphs (TDGs) are used to understand the temporal relations between events mentioned in a document and to improve downstream tasks such as timeline creation and time-aware summarization. |
| Approach: | They propose a temporal dependency graph parser that takes input from a text document and produces a graph that incorporates longer range dependencies. |
| Outcome: | The proposed framework outperforms existing models on three datasets and improves tasks such as timeline creation, time-aware summarization, and temporal information extraction. |
Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)
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| Challenge: | Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge. |
| Approach: | They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality. |
| Outcome: | The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets. |
Table Fact Verification with Structure-Aware Transformer (2020.emnlp-main)
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| Challenge: | Pre-trained models cannot be used to encode semi-structured data because of their nature. |
| Approach: | They propose a Structure-Aware Transformer which injects table structural information into mask . method could combine symbolic and linguistic reasoning, they propose . |
| Outcome: | The proposed method outperforms baseline on a large scale table verification dataset. |
Pushdown Layers: Encoding Recursive Structure in Transformer Language Models (2023.emnlp-main)
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| Challenge: | Pushdown Layers model recursive state via stack tape that tracks estimated depths of tokens in incremental parsing . pushdown layers are drop-in replacement for standard self-attention . recursion is a key component of many aspects of intelligent behavior, authors say . |
| Approach: | They propose a self-attention layer that models recursive state via a stack tape . Pushdown Layers is a drop-in replacement for standard self- attention . |
| Outcome: | The proposed self-attention layer improves on parse tasks with a recursive-state model . it can model recursion using a stack tape that tracks estimated depths of tokens . |
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)
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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
| Challenge: | Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios. |
| Approach: | They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers . |
| Outcome: | The proposed model outperforms baselines and class transfer models in low-resource scenarios. |
Recursive Tree-Structured Self-Attention for Answer Sentence Selection (2021.acl-long)
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| Challenge: | Recent top-performing models in Answer Sentence Selection use self-attention and transfer learning, but not syntactic structure. |
| Approach: | They propose a recursive, tree-structured self-attention model that can represent all levels of syntactic parse trees with only one additional layer. |
| Outcome: | The proposed model can represent all levels of syntactic parse trees with only one additional layer without transfer learning. |
Revisiting Simple Neural Probabilistic Language Models (2021.naacl-main)
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| Challenge: | Recent advances in language modeling have been driven not only by advances in neural architectures, but also through hardware and optimization improvements. |
| Approach: | They revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003) which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. |
| Outcome: | The proposed model performs better on word-level language model benchmarks than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. |
Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have used Graph Neural Networks (GNNs) to encode language knowledge into token embeddings. |
| Approach: | They propose a multi-level community-awareness Graph Neural Network layer to jointly model local and global relationships between words and their linguistic roles in multiple communities. |
| Outcome: | The proposed method reduces time complexity in very long sentences while preserving the original meaning. |
CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to document-level neural machine translation focus on integrating context into translation, but they focus on the way of integrating contextual information into translation. |
| Approach: | They propose a document-level neural machine translation framework that models cohesion devices from two perspectives: Cohesion Device Masking and Cohetion Attention Focusing. |
| Outcome: | The proposed model outperforms state-of-the-art document-level neural machine translation baselines on three benchmark datasets. |
Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages (2025.emnlp-main)
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| Challenge: | Encoder transformer models compress information from all tokens into a single [CLS] token to represent global context. |
| Approach: | They propose a 1-D convolution module that augments token representations with multi-scale local features to improve performance. |
| Outcome: | Experiments on five diverse tasks show that the proposed framework outperforms baseline models by 1% to 14% while maintaining efficiency. |