# AD3: Attentive Deep Document Dater

arXiv: https://arxiv.org/abs/1902.02161 · HTML: https://mlsanity.com/paper/1902.02161/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Time Masking for Temporal Language Models](https://mlsanity.com/paper/2110.06366/) (2021)
- [Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End](https://mlsanity.com/paper/2105.07071/) (2021)
- [Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes](https://mlsanity.com/paper/2112.09742/) (2021)
- [Unified Modeling of Multi-Domain Multi-Device ASR Systems](https://mlsanity.com/paper/2205.06655/) (2022)
- [Improving RNN-T ASR Performance with Date-Time and Location Awareness](https://mlsanity.com/paper/2106.06183/) (2021)
- [Recovering Temporal and Geographic Signals from Language Model Embeddings](https://mlsanity.com/paper/2609.05721/) (2026)

## Abstract (arXiv)

Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can't be trusted. Thus, predicting creation time from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines.