# TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy

arXiv: https://arxiv.org/abs/2402.01714 · HTML: https://mlsanity.com/paper/2402.01714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 63 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://mlsanity.com/paper/2109.03910/) (2021)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019)
- [Data-to-text Generation with Entity Modeling](https://mlsanity.com/paper/1906.03221/) (2019)
- [A Closer Look at How Fine-tuning Changes BERT](https://mlsanity.com/paper/2106.14282/) (2021)
- [Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity](https://mlsanity.com/paper/2004.06577/) (2020)
- [Pragmatically Informative Text Generation](https://mlsanity.com/paper/1904.01301/) (2019)
- [DirectGPT: A Direct Manipulation Interface to Interact with Large Language Models](https://mlsanity.com/paper/2310.03691/) (2023)
- [What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment](https://mlsanity.com/paper/1509.00838/) (2015)
- [Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2001.11003/) (2020)
- [Augmenting Neural Response Generation with Context-Aware Topical Attention](https://mlsanity.com/paper/1811.01063/) (2018)
- [Control Prefixes for Parameter-Efficient Text Generation](https://mlsanity.com/paper/2110.08329/) (2021)
- [Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs](https://mlsanity.com/paper/2307.07312/) (2023)
- [Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation](https://mlsanity.com/paper/1908.03067/) (2019)
- [Structure-Infused Copy Mechanisms for Abstractive Summarization](https://mlsanity.com/paper/1806.05658/) (2018)

## Abstract (arXiv)

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.