| Challenge: | Existing work has extended recurrent neural networks to model lattice inputs but these models suffer from slow computation speeds. |
| Approach: | They propose to extend the paradigm of self-attention to handle lattice inputs by adding probabilistic reachability masks that incorporate latticae structure into the model and support lattics if available. |
| Outcome: | The proposed model outperforms baseline models while being much faster to compute than previous models. |
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Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models (2021.naacl-main)
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| Challenge: | Pre-trained language models process text as a sequence of characters, ignoring more coarse granularity, e.g., words. |
| Approach: | They propose a new pre-training paradigm for Chinese that incorporates word representations along with characters and can model a sentence in a multi-granular manner. |
| Outcome: | The proposed model can bring an average increase of 1.5% under the 12-layer setting, which achieves new state-of-the-art among base-size models on the CLUE benchmarks. |
Rethinking Self-Attention: Towards Interpretability in Neural Parsing (2020.findings-emnlp)
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| Challenge: | Recent work shows that attention mechanisms provide arguably explainable attention distributions that can help to interpret predictions. |
| Approach: | They propose a new self-attention layer where attention heads represent labels. |
| Outcome: | The proposed model obtains state-of-the-art results on the Penn Treebank and Chinese Treebank. |
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. |
Theoretical Limitations of Self-Attention in Neural Sequence Models (2020.tacl-1)
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| Challenge: | Existing work suggests that the computational capabilities of self-attention to model hierarchical structures are limited. |
| Approach: | They investigate the computational power of self-attention to model formal languages . they show strong theoretical limitations of self attention to model periodic finite-state languages unless the number of layers or heads increases with input length. |
| Outcome: | The proposed models can model periodic finite-state languages, nor hierarchical structure unless the number of layers or heads increases with input length. |
Syntax-Based Attention Masking for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Existing approaches to extend transformers to source-side trees are linearized into sequences, but they are limited by positional encodings. |
| Approach: | They propose a method for extending transformers to source-side trees by using masks based on tree positions . they define a number of masks that limit self-attention based upon relationships among tree nodes . |
| Outcome: | The proposed method improves on translations from English to germany and English to english and germany by +2.1 BLEU. |
Linguistically-Informed Self-Attention for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models of semantic role labeling use no explicit linguistic features. prior work has shown that syntax trees can dramatically improve SRL decoding. |
| Approach: | They propose a neural network model that incorporates syntax using only raw tokens . they show that LISA out-performs the state-of-the-art with contextually-encoded word representations a 1.0 F1 on newswire and 2.0 F1 in out-of domain text . |
| Outcome: | The proposed model outperforms the state-of-the-art model with word embeddings and predicted predicates. |
Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)
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Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu
| Challenge: | Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers. |
| Approach: | They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights. |
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Leveraging Local and Global Patterns for Self-Attention Networks (P19-1)
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| Challenge: | Existing approaches to integrate local and global information into self-attention networks have been criticized for overlooking neighboring information. |
| Approach: | They propose a hybrid attention mechanism to leverage local and global information . they use a gating scalar to integrate both sources of information based on local contexts . |
| Outcome: | The proposed approach improves on translation tasks and shows that the two types of contexts are complementary. |
Self-Attention with Relative Position Representations (N18-2)
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| Challenge: | Recent approaches to sequence to sequence learning leverage recurrence, convolution, attention or combination of recurrent and convolutional neural networks. |
| Approach: | They propose an approach that extends the self-attention mechanism to consider representations of relative positions, or distances between sequence elements. |
| Outcome: | The proposed approach yields 1.3 BLEU and 0.3 BLUE on translation tasks . it is based on a relation-aware self-attention mechanism that can generalize to arbitrary graph-labeled inputs. |
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models (2021.acl-long)
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| Challenge: | Recent work has shown that convolutions have been successful in natural language learning. |
| Approach: | They propose a convolutional approach to construct relative position embeddings in self-attention layers and propose 'compact attention' they propose multiple ways to integrate convolutions into Transformer self- attention. |
| Outcome: | The proposed composite attention improves performance on multiple downstream tasks, replacing absolute position embeddings, and is more expressive than convolutions in NLP. |