Search Results for author: Jannis Bulian

Found 10 papers, 4 papers with code

Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation

1 code implementation15 Feb 2022 Jannis Bulian, Christian Buck, Wojciech Gajewski, Benjamin Boerschinger, Tal Schuster

The predictions of question answering (QA) systems are typically evaluated against manually annotated finite sets of one or more answers.

Question Answering

Fool Me Twice: Entailment from Wikipedia Gamification

1 code implementation NAACL 2021 Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, Jordan Boyd-Graber

We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game.

Meta Answering for Machine Reading

no code implementations11 Nov 2019 Benjamin Borschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, Lierni Sestorain Saralegu

We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment.

Question Answering Reading Comprehension

Multi-agent query reformulation: Challenges and the role of diversity

no code implementations ICLR Workshop drlStructPred 2019 Rodrigo Nogueira, Jannis Bulian, Massimiliano Ciaramita

We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation.

Question Answering reinforcement-learning +1

Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation

no code implementations ICLR 2019 Rodrigo Nogueira, Jannis Bulian, Massimiliano Ciaramita

We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering.

Question Answering reinforcement-learning

Analyzing Language Learned by an Active Question Answering Agent

no code implementations23 Jan 2018 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].

Information Retrieval Question Answering +1

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

2 code implementations ICLR 2018 Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang

The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer.

Frame Information Retrieval +2

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