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Analyzing Language Learned by an Active Question Answering Agent

2018-01-23 · Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang

We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017]. In ActiveQA, question answering is framed as a reinforcement learning task in which an agent sits between the user and a black box question-answering system. The agent learns to reformulate the user's questions to elicit the optimal answers. It probes the system with many versions of a question that are generated via a sequence-to-sequence question reformulation model, then aggregates the returned evidence to find the best answer. This process is an instance of \emph{machine-machine} communication. The question reformulation model must adapt its language to increase the quality of the answers returned, matching the language of the question answering system. We find that the agent does not learn transformations that align with semantic intuitions but discovers through learning classical information retrieval techniques such as tf-idf re-weighting and stemming.

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Tasks

Information Retrieval Question Answering reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Retrieval

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

2017-05-22 · ICLR 2018 1 · Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski 외

We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box QA system and learns to reformulate qu…

Information Retrieval Question Answering reinforcement-learning Reinforcement Learning+2

Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection

2025-12-15 · Juil Koo, Daehyeon Choi, Sangwoo Youn, Phillip Y. Lee 외 arxiv

Vision Language Models (VLMs) excel at visual question answering (VQA) but remain limited to snapshot vision, reasoning from static images. In contrast, embodied agents require ambulatory vision, actively moving to obtai…

Visual Question Answering

GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval

2021-04-26 · EMNLP (MRQA) 2021 11 · Timo Möller, Julian Risch, Malte Pietsch

A major challenge of research on non-English machine reading for question answering (QA) is the lack of annotated datasets. In this paper, we present GermanQuAD, a dataset of 13,722 extractive question/answer pairs. To i…

Passage Retrieval Question Answering Reading Comprehension Retrieval

Momentum Contrastive Pre-training for Question Answering

2022-12-12 · Minda Hu, Muzhi Li, Yasheng Wang, Irwin King

Existing pre-training methods for extractive Question Answering (QA) generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. In…

Benchmarking Contrastive Learning Extractive Question-Answering Natural Questions+1

Interactive Question Answering Systems: Literature Review

2022-09-04 · Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio 외

Question answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their query by presenting thei…

Question Answering