mlsanityData to 7 Oct 2026

Attention Strategies for Multi-Source Sequence-to-Sequence Learning

arXiv 1704.06567 ↗First posted 21 Apr 2017cs.CL cs.NEcited by 32

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Abstract (arXiv)

Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2017.

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Citing papers per year: 2021: 12; 2022: 7; 2023: 6; 2024: 4; 2025: 2; 2026: 1.

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  1. 2023Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOMcited by 58
  2. 2021Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translationcited by 44
  3. 2021Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarizationcited by 32
  4. 2022On Vision Features in Multimodal Machine Translationcited by 31
  5. 2022Neural Machine Translation with Phrase-Level Universal Visual Representationscited by 26
  6. 2023Exploring Better Text Image Translation with Multimodal Codebookcited by 23
  7. 2022LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translationcited by 21
  8. 2021Cross-lingual Visual Pre-training for Multimodal Machine Translationcited by 19
  9. 2021See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarizationcited by 18
  10. 2022Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translationcited by 11
  11. 2025AFRIDOC-MT: Document-level MT Corpus for African Languagescited by 11
  12. 2021Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-trainingcited by 10
20 more
  1. 2021Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Informationcited by 9
  2. 2021Attention-based Multi-hypothesis Fusion for Speech Summarizationcited by 9
  3. 2024On Measuring Context Utilization in Document-Level MT Systemscited by 9
  4. 2024AnyTrans: Translate AnyText in the Image with Large Scale Modelscited by 8
  5. 2021Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translationcited by 7
  6. 2021Text is no more Enough! A Benchmark for Profile-based Spoken Language Understandingcited by 7
  7. 2022Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translationcited by 7
  8. 2023Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contentscited by 7
  9. 2024A Survey on Multi-modal Machine Translation: Tasks, Methods and Challengescited by 6
  10. 2022Low-resource Neural Machine Translation with Cross-modal Alignmentcited by 5
  11. 2023BASS: Block-wise Adaptation for Speech Summarizationcited by 4
  12. 2021ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpuscited by 3
  13. 2023Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairscited by 3
  14. 2021Visual Cues and Error Correction for Translation Robustnesscited by 2
  15. 2022Supervised Visual Attention for Simultaneous Multimodal Machine Translationcited by 2
  16. 2021ICAF: Iterative Contrastive Alignment Framework for Multimodal Abstractive Summarizationcited by 1
  17. 2024How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?cited by 1
  18. 2023CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarizationcited by 0
  19. 2025Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decodingcited by 0
  20. 2026Multimodal Abstractive Summarization of Instructional Videos with Vision-Language Modelscited by 0