Papers by Luyang Huang
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward (2020.acl-main)
Copied to clipboard
| Challenge: | Abstractive summarization models for document encoders suffer from fabricated content and are often near-extractive. |
| Approach: | They propose a framework for abstractive summarization with Graph-Augmentation and semantic-driven RewarD that uses a sequential document encoder and a graph-structured encoder to maintain the global context and local characteristics of entities. |
| Outcome: | The proposed framework produces higher ROUGE scores than a variant without knowledge graph on New York Times and CNN/Daily Mail datasets. |
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. |
| Approach: | They propose a framework for multimodal machine translation that utilizes large-scale non-triple data and a multimodal translation dataset. |
| Outcome: | The proposed method can significantly improve translation performance with more non-triple data. |
Efficient Attentions for Long Document Summarization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models that use full attentions have quadratic computational and memory complexities, and are too costly for long documents. |
| Approach: | They propose an efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. |
| Outcome: | The proposed model can process ten times more tokens than current models that use full attentions. |
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation (2023.findings-acl)
Copied to clipboard
Liyan Kang, Luyang Huang, Ningxin Peng, Peihao Zhu, Zewei Sun, Shanbo Cheng, Mingxuan Wang, Degen Huang, Jinsong Su
| Challenge: | Existing datasets focus on captions describing images or videos, which are not large and diverse enough. |
| Approach: | They propose a large-scale video subtitle translation dataset to facilitate multi-modality machine translation. |
| Outcome: | The proposed dataset is 10 times larger than the widely used *How2* and *VaTeX* datasets. |
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods to compress long contexts have degraded dramatically as compression ratios increase, sometimes even falling to the closed-book level. |
| Approach: | They propose a query-guided compression method that preserves key information within the compressed context. |
| Outcome: | The proposed method can consistently perform well even at high compression ratios, and offers significant benefits in terms of inference cost and throughput. |
Task-oriented Word Embedding for Text Classification (C18-1)
Copied to clipboard
| Challenge: | Existing word embeddings only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. |
| Approach: | They propose a task-oriented word embedding method that regularizes the distribution of words to enable a clear classification boundary. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on a text classification task. |
An Entity-Driven Framework for Abstractive Summarization (D19-1)
Copied to clipboard
| Challenge: | Popular neural summarization models produce incoherent and unfaithful summaries . however, their outputs are often incohérent and incoerent . |
| Approach: | They propose a system for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on New York Times and CNN/Daily Mail datasets. |
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing vision-and-language generation models cannot utilize pair-wise images and text through bi-directional generation due to the limitations of the model structure and pre-training objectives. |
| Approach: | They propose a framework which unifies vision-and-language generation as sequence generation problems. |
| Outcome: | The proposed framework achieves better performance than variants trained with uni-directional generation objectives or the variant without the commitment loss on image captioning and text-to-image generation datasets. |