Search Results for author: Massimiliano Ciaramita

Found 13 papers, 2 papers with code

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.

Information Seeking Question Answering +1

On Identifiability in Transformers

no code implementations ICLR 2020 Gino Brunner, Yang Liu, Damián Pascual, Oliver Richter, Massimiliano Ciaramita, Roger Wattenhofer

We show that, for sequences longer than the attention head dimension, attention weights are not identifiable.

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.

Language understanding Question Answering +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

Zero-Shot Dual Machine Translation

1 code implementation25 May 2018 Lierni Sestorain, Massimiliano Ciaramita, Christian Buck, Thomas Hofmann

Our method can obtain improvements also on the setting where a small amount of parallel data for the zero-shot language pair is available.

Machine Translation Translation

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

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.

Information Retrieval Question Answering

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