# Location Aware Modular Biencoder for Tourism Question Answering

arXiv: https://arxiv.org/abs/2401.02187 · HTML: https://mlsanity.com/paper/2401.02187/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://mlsanity.com/paper/2004.02984/) (2020)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index](https://mlsanity.com/paper/1906.05807/) (2019)
- [SG-Net: Syntax-Guided Machine Reading Comprehension](https://mlsanity.com/paper/1908.05147/) (2019)
- [Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering](https://mlsanity.com/paper/1905.05733/) (2019)
- [Learning Opinion Summarizers by Selecting Informative Reviews](https://mlsanity.com/paper/2109.04325/) (2021)
- [Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions](https://mlsanity.com/paper/2105.09392/) (2021)
- [GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level](https://mlsanity.com/paper/1908.07855/) (2019)
- [Geographic Adaptation of Pretrained Language Models](https://mlsanity.com/paper/2203.08565/) (2022)
- [An Updated Duet Model for Passage Re-ranking](https://mlsanity.com/paper/1903.07666/) (2019)
- [Template-Based Question Answering over Linked Geospatial Data](https://mlsanity.com/paper/2007.07060/) (2020)
- [Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering](https://mlsanity.com/paper/2005.00038/) (2020)
- [Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization](https://mlsanity.com/paper/1404.7152/) (2014)
- [Joint Spatio-Textual Reasoning for Answering Tourism Questions](https://mlsanity.com/paper/2009.13613/) (2020)
- [Translating Place-Related Questions to GeoSPARQL Queries](https://mlsanity.com/paper/2205.03067/) (2022)

## Built on by (most cited first, 2 of 2)

- [GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations](https://mlsanity.com/paper/2503.16683/) (2025)
- [CAIRNS: Balancing Readability and Scientific Accuracy in Climate Adaptation Question Answering](https://mlsanity.com/paper/2512.02251/) (2025)

## Abstract (arXiv)

Answering real-world tourism questions that seek Point-of-Interest (POI) recommendations is challenging, as it requires both spatial and non-spatial reasoning, over a large candidate pool. The traditional method of encoding each pair of question and POI becomes inefficient when the number of candidates increases, making it infeasible for real-world applications. To overcome this, we propose treating the QA task as a dense vector retrieval problem, where we encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity. We use pretrained language models (PLMs) to encode textual information, and train a location encoder to capture spatial information of POIs. Experiments on a real-world tourism QA dataset demonstrate that our approach is effective, efficient, and outperforms previous methods across all metrics. Enabled by the dense retrieval architecture, we further build a global evaluation baseline, expanding the search space by 20 times compared to previous work. We also explore several factors that impact on the model's performance through follow-up experiments. Our code and model are publicly available at https://github.com/haonan-li/LAMB.