| Challenge: | Existing alignment methods operate at a single, predefined level and cannot learn to align texts at sentence and document levels. |
| Approach: | They propose a learning approach that equips hierarchical attention encoders for representing documents with a cross-document attention component, enabling structural comparisons across different levels. |
| Outcome: | The proposed model outperforms existing hierarchical, attention encoders on citation recommendation and plagiarism detection tasks. |
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| Challenge: | Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner. |
| Approach: | They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. |
| Outcome: | The proposed model improves on the WikiSum dataset and can process multiple documents in a hierarchical manner. |
Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)
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| Challenge: | Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality. |
| Approach: | They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model. |
| Outcome: | The proposed model achieves the state-of-the-art (SOTA) performance on four out of five language pairs. |
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) graphs embed the semantics of a sentence in a directed acyclic graph, where concepts are represented by nodes, semantic relations between concepts by edges, and the co-references by reentrant nodes. |
| Approach: | They propose a novel aligner for Abstract Meaning Representation graphs that scales cross-lingually and can align units and spans in sentences of different languages. |
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Document-Level Neural Machine Translation with Hierarchical Attention Networks (D18-1)
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| Challenge: | Neural machine translation (NMT) can be improved by including document-level contextual information. |
| Approach: | They propose a hierarchical attention model that captures document-level contextual information and conditioning on the NMT model’s own hidden states. |
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Dual Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Experimental results on three real-world cross-lingual entity alignment datasets have shown the effectiveness of DAEA. |
| Approach: | They propose a dual attention network for cross-lingual entity alignment . they use relation-aware graph attention and hierarchical attention to solve this problem . |
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Dual Attention Model for Citation Recommendation (2020.coling-main)
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| Challenge: | Existing methods for recommending citations suffer from severe information loss . citation recommender methods do not consider the section of the paper for which the user is writing and for which they need to find a citation . |
| Approach: | They propose a novel embedding-based neural network to recommend citations during manuscript preparation. |
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Selective Attention for Context-aware Neural Machine Translation (N19-1)
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| Challenge: | Recent work in context-aware NMT considers only a few previous sentences as context . current systems fail to achieve fluent, good quality translation for a full document . |
| Approach: | They propose a top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context. |
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DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning (2025.findings-acl)
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| Challenge: | Existing multimodal sentence representation learning methods focus on aligning images and text at a coarse level, resulting in cross-modal misalignment bias and intra-modal semantic divergence. |
| Approach: | They propose a dual-level alignment learning framework for multimodal sentence representation learning that promotes cross-modal and intra-modal alignment. |
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Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover’s Distance (2020.aacl-main)
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| Challenge: | Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They propose an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. |
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Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)
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| Challenge: | Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers. |
| Approach: | They evaluate a recent linear attention model with a sentential gate to promote a recency inductive bias and compare it to open-source document translation. |
| Outcome: | The proposed model significantly improves translation quality on IWSLT 2015 and OpenSubtitles 2018 with similar or better BLEU scores. |