Papers with injective
Lost in Machine Translation: A Method to Reduce Meaning Loss (N19-1)
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| Challenge: | state-of-the-art translation systems often fail in preserving meaning . ambiguity between source and target languages can cause translation problems . |
| Approach: | They propose to use a pre-trained neural sequence-to-sequence model to define a less ambiguous translation system. |
| Outcome: | The proposed system preserves meaning in two languages without compromising translation quality. |
Constituent Parsing as Sequence Labeling (D18-1)
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| Challenge: | Constituent parsing is a core problem in NLP where the goal is to obtain the syntactic structure of sentences expressed as a phrase structure tree. |
| Approach: | They propose a method to reduce constituent parsing to sequence labeling by using a tree with unary branches. |
| Outcome: | The proposed method outperforms the Vinyals et al. (2015) sequence-to-sequence parser by 90% on the PTB and CTB treebanks. |
Manifold-Preserving Transformers are Effective for Short-Long Range Encoding (2023.findings-emnlp)
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| Challenge: | Multi-head self-attention-based Transformers have shown promise in different learning tasks . but encoders of Transformers and their variants fail to preserve layer-wise contextual information . |
| Approach: | They propose an encoder model that guarantees a theoretical bound for layer-wise distance preservation between a pair of tokens. |
| Outcome: | The proposed model preserves equivalence between tokens and performs better than Transformers. |