# Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

arXiv: https://arxiv.org/abs/1607.06275 · HTML: https://mlsanity.com/paper/1607.06275/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2016.

## Built on by (most cited first, 30 of 35)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation](https://mlsanity.com/paper/2104.12369/) (2021)
- [WebSRC: A Dataset for Web-Based Structural Reading Comprehension](https://mlsanity.com/paper/2101.09465/) (2021)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders](https://mlsanity.com/paper/2106.13736/) (2021)
- [RaFe: Ranking Feedback Improves Query Rewriting for RAG](https://mlsanity.com/paper/2405.14431/) (2024)
- [ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2112.12731/) (2021)
- [WeLM: A Well-Read Pre-trained Language Model for Chinese](https://mlsanity.com/paper/2209.10372/) (2022)
- [Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector](https://mlsanity.com/paper/2406.11277/) (2024)
- [VocalBench: Benchmarking the Vocal Conversational Abilities for Speech Interaction Models](https://mlsanity.com/paper/2505.15727/) (2025)
- [SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding](https://mlsanity.com/paper/2308.10529/) (2023)
- [CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models](https://mlsanity.com/paper/2403.03514/) (2024)
- [When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding](https://mlsanity.com/paper/2506.05551/) (2025)
- [Wizard of Search Engine: Access to Information Through Conversations with Search Engines](https://mlsanity.com/paper/2105.08301/) (2021)
- [Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario](https://mlsanity.com/paper/2406.12429/) (2024)
- [TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition](https://mlsanity.com/paper/2408.09856/) (2024)
- [GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation](https://mlsanity.com/paper/2302.14401/) (2023)
- [1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?](https://mlsanity.com/paper/2406.14721/) (2024)
- [Towards a Better Understanding Human Reading Comprehension with Brain Signals](https://mlsanity.com/paper/2108.01360/) (2021)
- [s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning](https://mlsanity.com/paper/2110.13640/) (2021)
- [Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework](https://mlsanity.com/paper/2212.10097/) (2022)
- [Large-Scale Diverse Synthesis for Mid-Training](https://mlsanity.com/paper/2508.01326/) (2025)
- [CodeSimpleQA: Scaling Factuality in Code Large Language Models](https://mlsanity.com/paper/2512.19424/) (2025)
- [LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points](https://mlsanity.com/paper/2508.01317/) (2025)
- [Knowledge Graph Question Answering via SPARQL Silhouette Generation](https://mlsanity.com/paper/2109.09475/) (2021)
- [Towards Robust Text Retrieval with Progressive Learning](https://mlsanity.com/paper/2311.11691/) (2023)
- [ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models](https://mlsanity.com/paper/2403.05132/) (2024)
- [Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.19209/) (2025)

## Abstract (arXiv)

While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system. To alleviate this problem, we propose a large scale human annotated real-world QA dataset WebQA with more than 42k questions and 556k evidences. As existing neural QA methods resolve QA either as sequence generation or classification/ranking problem, they face challenges of expensive softmax computation, unseen answers handling or separate candidate answer generation component. In this work, we cast neural QA as a sequence labeling problem and propose an end-to-end sequence labeling model, which overcomes all the above challenges. Experimental results on WebQA show that our model outperforms the baselines significantly with an F1 score of 74.69% with word-based input, and the performance drops only 3.72 F1 points with more challenging character-based input.