Papers with METEOR scores
MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities (2024.naacl-long)
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| Challenge: | Decoding continuous language from brain activity is a formidable but promising field of research . previous attempts to map brain activity to text relied on learning to encode brain activity . |
| Approach: | They propose a method that maps brain activity to text embeddings by directly comparing them with predicted brain responses. |
| Outcome: | The proposed method outperforms the current state-of-the-art model showing improvements on BLEU and METEOR scores. |
Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)
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| Challenge: | Existing methods for extractive and abstractive summarization are far from human performance. |
| Approach: | They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context. |
| Outcome: | The proposed model outperforms previous models on ROUGE-1, ROUGEE-2 and METEOR scores on two datasets of scientific papers. |
A Simple and Effective Unified Encoder for Document-Level Machine Translation (2020.acl-main)
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| Challenge: | Existing models for document-level machine translation use two separate encoders to model the source sentences and document- level contexts. |
| Approach: | They propose a unified encoder that can outperform existing models of dual-encoder models . they propose to use document-level contexts to model the interaction between the contexts and the source sentences . |
| Outcome: | The proposed model outperforms baseline models of dual-encoder models in terms of BLEU and METEOR scores. |
ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos (2024.emnlp-main)
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| Challenge: | Existing studies on question generation from videos are mostly focused on generating questions about common objects and attributes. |
| Approach: | They propose a model architecture combining Transformers, rich context signals and a combination of cross-entropy and contrastive loss function to encourage entity-centric question generation. |
| Outcome: | The proposed system yields BLEU, ROUGE, CIDEr, and METEOR scores of 71.3, 78.6, 7.31, and 81.9. |
Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization (2024.lrec-main)
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| Challenge: | Existing methods to improve language models require manual ranking and annotators. |
| Approach: | They propose a self-supervised text ranking approach for applying Proximal-Policy-Optimization to fine-tune language models while eliminating the need for human annotators. |
| Outcome: | The proposed method significantly outperforms baselines regarding BLEU, GLEU, and METEOR scores on three tasks and is consistent with humans. |