# End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

arXiv: https://arxiv.org/abs/1610.09996 · HTML: https://mlsanity.com/paper/1610.09996/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

## Built on by (most cited first, 4 of 4)

- [Self-Supervised Test-Time Learning for Reading Comprehension](https://mlsanity.com/paper/2103.11263/) (2021)
- [Improving Question Answering Performance Using Knowledge Distillation and Active Learning](https://mlsanity.com/paper/2109.12662/) (2021)
- [Universal Model in Online Customer Service](https://mlsanity.com/paper/2402.15666/) (2024)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)

## Abstract (arXiv)

This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions. DCR is able to predict answers of variable lengths, whereas previous neural RC models primarily focused on predicting single tokens or entities. DCR encodes a document and an input question with recurrent neural networks, and then applies a word-by-word attention mechanism to acquire question-aware representations for the document, followed by the generation of chunk representations and a ranking module to propose the top-ranked chunk as the answer. Experimental results show that DCR achieves state-of-the-art exact match and F1 scores on the SQuAD dataset.