# Interactive Language Learning by Question Answering

arXiv: https://arxiv.org/abs/1908.10909 · HTML: https://mlsanity.com/paper/1908.10909/

## Key facts

- Cited by 19 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 19 of 19)

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- [Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021](https://mlsanity.com/paper/2205.02388/) (2022)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment](https://mlsanity.com/paper/2110.06536/) (2021)
- [A Survey of Text Games for Reinforcement Learning informed by Natural Language](https://mlsanity.com/paper/2109.09478/) (2021)
- [Language and Culture Internalisation for Human-Like Autotelic AI](https://mlsanity.com/paper/2206.01134/) (2022)
- [Hey AI, Can You Solve Complex Tasks by Talking to Agents?](https://mlsanity.com/paper/2110.08542/) (2021)
- [Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations](https://mlsanity.com/paper/2405.13828/) (2024)
- [Interactive Question Answering Systems: Literature Review](https://mlsanity.com/paper/2209.01621/) (2022)
- [Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers](https://mlsanity.com/paper/2307.09455/) (2023)
- [ScriptWorld: Text Based Environment For Learning Procedural Knowledge](https://mlsanity.com/paper/2307.03906/) (2023)
- [An Analysis of Deep Reinforcement Learning Agents for Text-based Games](https://mlsanity.com/paper/2209.04105/) (2022)
- [A Data Source for Reasoning Embodied Agents](https://mlsanity.com/paper/2309.07974/) (2023)
- [Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans](https://mlsanity.com/paper/2310.09017/) (2023)
- [How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?](https://mlsanity.com/paper/2409.02253/) (2024)
- [GenQuest: An LLM-based Text Adventure Game for Language Learners](https://mlsanity.com/paper/2510.04498/) (2025)

## Abstract (arXiv)

Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking component of comprehension. Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring. Thus, models can achieve strong performance through simple word- and phrase-based pattern matching. We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit). In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions. QAit poses questions about the existence, location, and attributes of objects found in the environment. The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.