TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
<|reference_start|>TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy: Data-to-text (D2T) generation is a crucial task in many natural language understanding (NLU) applications and forms the foundation of task-oriented dialog systems. In the context of conversational AI solutions that can work directly with local data on the user's device, architectures utilizing large pre-trained language models (PLMs) are impractical for on-device deployment due to a high memory footprint. To this end, we propose TrICy, a novel lightweight framework for an enhanced D2T task that generates text sequences based on the intent in context and may further be guided by user-provided triggers. We leverage an attention-copy mechanism to predict out-of-vocabulary (OOV) words accurately. Performance analyses on E2E NLG dataset (BLEU: 66.43%, ROUGE-L: 70.14%), WebNLG dataset (BLEU: Seen 64.08%, Unseen 52.35%), and our Custom dataset related to text messaging applications, showcase our architecture's effectiveness. Moreover, we show that by leveraging an optional trigger input, data-to-text generation quality increases significantly and achieves the new SOTA score of 69.29% BLEU for E2E NLG. Furthermore, our analyses show that TrICy achieves at least 24% and 3% improvement in BLEU and METEOR respectively over LLMs like GPT-3, ChatGPT, and Llama 2. We also demonstrate that in some scenarios, performance improvement due to triggers is observed even when they are absent in training.<|reference_end|>
@article{agarwal2024tricy:,
    title={TrICy: Trigger-guided Data-to-text Generation with Intent aware
  Attention-Copy},
    author={Vibhav Agarwal, Sourav Ghosh, Harichandana BSS, Himanshu Arora, Barath
  Raj Kandur Raja},
    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing,
  vol. 32, pp. 1173-1184, 2024},
    year={2024},
    doi={10.1109/TASLP.2024.3353574},
    archivePrefix={arXiv},
    eprint={2402.01714},
    primaryClass={cs.CL cs.AI cs.LG}
}