Papers with attention models
Efficient Transformer Knowledge Distillation: A Performance Review (2023.emnlp-industry)
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| Challenge: | Pretrained transformer language models have been gaining popularity in the field of natural language processing . however, there is no study into the intersection of these two fields . |
| Approach: | They propose a method to extract knowledge from transformers to produce high-performing efficient attention models with low costs. |
| Outcome: | The proposed model compression method preserves up to 98.6% of original model performance across short-context tasks and up to 95.8% on long-concept Named Entity Recognition tasks while decreasing inference times by up to 57%. |
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference (2020.emnlp-main)
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Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen
| Challenge: | Existing studies show that multi-head attention is an effective module in deep neural networks, but there are no explicit mechanisms guaranteeing this property. |
| Approach: | They propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model’s expressiveness. |
| Outcome: | The proposed approach improves the repulsiveness in multi-head attention and strengthens model’s expressiveness. |
Interrogating the Explanatory Power of Attention in Neural Machine Translation (D19-56)
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| Challenge: | Attention models are often used to justify the model’s decision in generating a token but it has not been rigorously established to what extent attention is a reliable source of information in NMT. |
| Approach: | They propose to use attention models to modify crucial aspects of the trained attention model to produce function and content words in the translation process. |
| Outcome: | The proposed models preserve function and content words in the translation process compared to state-of-the-art models. |
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
| Approach: | They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. |
| Outcome: | The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks. |
A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis (C18-1)
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| Challenge: | Existing approaches to model aspect-level sentiment analysis neglect the position information of aspect terms. |
| Approach: | They propose a position-aware bidirectional attention network (PBAN) that mutually models the relation between aspect terms and sentences by employing bidirectional mechanism. |
| Outcome: | The proposed model can distinguish sentiment polarity of aspect terms in sentences . it can also model relation between aspect terms and sentences based on position information . |
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis (C18-1)
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| Challenge: | Existing attention models do not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. |
| Approach: | They propose a lexicon-based supervised attention model which allows a neural network to focus on the sentiment content, thus generating sentiment-informative representations. |
| Outcome: | The proposed model outperforms existing models on three large-scale sentiment classification datasets. |
Modeling Recurrence for Transformer (N19-1)
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| Challenge: | Existing studies show that the lack of recurrence modeling hinders the development of a translation model. |
| Approach: | They propose to model recurrence for Transformer with an additional recurrent encoder. |
| Outcome: | The proposed model outperforms the deep model on EnglishGerman and ChineseEnglish translation tasks. |
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation (D18-1)
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Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, Silvio Savarese
| Challenge: | Existing models for translating free-form natural language instructions to a high-level plan for behavioral robot navigation are difficult due to the variability in the way people describe routes. |
| Approach: | They propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for robot navigation. |
| Outcome: | The proposed model significantly outperforms baseline approaches on a new dataset containing 10,050 pairs of navigation instructions. |
Multi-grained Attention with Object-level Grounding for Visual Question Answering (P19-1)
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| Challenge: | Current approaches to visual question answering train attention models from coarse-grained associations between sentences and images, which fail on small objects or uncommon concepts. |
| Approach: | They propose a multi-grained attention method that learns explicit word-object correspondence by word-level attention complementary to the sentence-image association. |
| Outcome: | The proposed method achieves competitive performance with state-of-the-art models on the VQA benchmark. |
ABC: Attention with Bounded-memory Control (2022.acl-long)
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Hao Peng, Jungo Kasai, Nikolaos Pappas, Dani Yogatama, Zhaofeng Wu, Lingpeng Kong, Roy Schwartz, Noah A. Smith
| Challenge: | Existing approaches to attention with bounded-memory control (ABC) have a quadratic complexity in sequence lengths, making it prohibitive for long sequences. |
| Approach: | They propose a new abstraction that bounds memory size to improve efficiency . they propose bounded-memory control, which connects several efficient attention variants . |
| Outcome: | The proposed approach outperforms existing approaches on language modeling, machine translation, and masked language model finetuning. |
GAttention: Gated Attention for the Detection of Abusive Language (2025.findings-emnlp)
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| Challenge: | Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues. |
| Approach: | They propose a Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task. |
| Outcome: | The novel gated attention mechanism addresses the limitations of existing attention models within the text classification task. |