Summary of Tricy: Trigger-guided Data-to-text Generation with Intent Aware Attention-copy, by Vibhav Agarwal et al.
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
by Vibhav Agarwal, Sourav Ghosh, Harichandana BSS, Himanshu Arora, Barath Raj Kandur Raja
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TrICy framework is a lightweight architecture for data-to-text generation that can be deployed on-device. It uses an attention-copy mechanism to accurately predict out-of-vocabulary words and generates text sequences based on intent and context, with optional trigger inputs to further guide the process. The model achieves state-of-the-art performance on several benchmark datasets, including E2E NLG (BLEU: 66.43%, ROUGE-L: 70.14%) and WebNLG (BLEU: Seen 64.08%, Unseen 52.35%). Additionally, it shows significant improvements over pre-trained language models like GPT-3, ChatGPT, and Llama 2 in terms of BLEU and METEOR scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TrICy is a new way to make computers understand and generate text based on what someone wants. It’s special because it can work with small amounts of data and doesn’t need huge language models to do its job. The researchers tested TrICy with different types of text and found that it does really well, even better than some other popular methods. They also showed that when they add a little extra information (called triggers) to help the computer understand what to say, TrICy gets even better. |
Keywords
* Artificial intelligence * Attention * Bleu * Gpt * Llama * Rouge * Text generation
