# Triangular Bidword Generation for Sponsored Search Auction

arXiv: https://arxiv.org/abs/2101.11349 · HTML: https://mlsanity.com/paper/2101.11349/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling](https://mlsanity.com/paper/1811.10996/) (2018)
- [Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information](https://mlsanity.com/paper/2010.03142/) (2020)
- [A Simple, Fast Diverse Decoding Algorithm for Neural Generation](https://mlsanity.com/paper/1611.08562/) (2016)
- [An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain](https://mlsanity.com/paper/1902.00592/) (2019)
- [Max-Pooling Dropout for Regularization of Convolutional Neural Networks](https://mlsanity.com/paper/1512.01400/) (2015)
- [Semantic Parsing with Semi-Supervised Sequential Autoencoders](https://mlsanity.com/paper/1609.09315/) (2016)
- [NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation](https://mlsanity.com/paper/1810.00671/) (2018)
- [Domain-Constrained Advertising Keyword Generation](https://mlsanity.com/paper/1902.10374/) (2019)
- [Triangular Architecture for Rare Language Translation](https://mlsanity.com/paper/1805.04813/) (2018)
- [Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation](https://mlsanity.com/paper/2007.06018/) (2020)

## Built on by (most cited first, 5 of 5)

- [From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning](https://mlsanity.com/paper/2408.08981/) (2024)
- [Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching](https://mlsanity.com/paper/2210.15459/) (2022)
- [LLMDistill4Ads: Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations at eBay](https://mlsanity.com/paper/2508.03628/) (2025)
- [BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations](https://mlsanity.com/paper/2505.19164/) (2025)
- [Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising](https://mlsanity.com/paper/2506.03827/) (2025)

## Abstract (arXiv)

Sponsored search auction is a crucial component of modern search engines. It requires a set of candidate bidwords that advertisers can place bids on. Existing methods generate bidwords from search queries or advertisement content. However, they suffer from the data noise in <query, bidword> and <advertisement, bidword> pairs. In this paper, we propose a triangular bidword generation model (TRIDENT), which takes the high-quality data of paired <query, advertisement> as a supervision signal to indirectly guide the bidword generation process. Our proposed model is simple yet effective: by using bidword as the bridge between search query and advertisement, the generation of search query, advertisement and bidword can be jointly learned in the triangular training framework. This alleviates the problem that the training data of bidword may be noisy. Experimental results, including automatic and human evaluations, show that our proposed TRIDENT can generate relevant and diverse bidwords for both search queries and advertisements. Our evaluation on online real data validates the effectiveness of the TRIDENT's generated bidwords for product search.