Search Results for author: Fabio Petroni

Found 29 papers, 13 papers with code

Open Vocabulary Extreme Classification Using Generative Models

no code implementations Findings (ACL) 2022 Daniel Simig, Fabio Petroni, Pouya Yanki, Kashyap Popat, Christina Du, Sebastian Riedel, Majid Yazdani

To develop systems that simplify this process, we introduce the task of open vocabulary XMC (OXMC): given a piece of content, predict a set of labels, some of which may be outside of the known tag set.

Classification Extreme Multi-Label Classification +3

Autoregressive Search Engines: Generating Substrings as Document Identifiers

1 code implementation22 Apr 2022 Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Wen-tau Yih, Sebastian Riedel, Fabio Petroni

Knowledge-intensive language tasks require NLP systems to both provide the correct answer and retrieve supporting evidence for it in a given corpus.

Information Retrieval

Learning To Recognize Procedural Activities with Distant Supervision

no code implementations26 Jan 2022 Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, Lorenzo Torresani

In this paper we consider the problem of classifying fine-grained, multi-step activities (e. g., cooking different recipes, making disparate home improvements, creating various forms of arts and crafts) from long videos spanning up to several minutes.

Action Classification Language Modelling +1

The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus

no code implementations18 Dec 2021 Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Dmytro Okhonko, Samuel Broscheit, Gautier Izacard, Patrick Lewis, Barlas Oğuz, Edouard Grave, Wen-tau Yih, Sebastian Riedel

In order to address the increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web scale knowledge, lack of structure, inconsistent quality, and noise.

GenIE: Generative Information Extraction

1 code implementation15 Dec 2021 Martin Josifoski, Nicola De Cao, Maxime Peyrard, Fabio Petroni, Robert West

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema.

Boosted Dense Retriever

no code implementations14 Dec 2021 Patrick Lewis, Barlas Oğuz, Wenhan Xiong, Fabio Petroni, Wen-tau Yih, Sebastian Riedel

DrBoost is trained in stages: each component model is learned sequentially and specialized by focusing only on retrieval mistakes made by the current ensemble.

Quantization

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

1 code implementation27 Sep 2021 Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel

By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use.

NetHack reinforcement-learning +1

CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN

no code implementations1 Apr 2021 Giorgio Barnabò, Giovanni Trappolini, Lorenzo Lastilla, Cesare Campagnano, Angela Fan, Fabio Petroni, Fabrizio Silvestri

The two main research threads in computer-based music generation are: the construction of autonomous music-making systems, and the design of computer-based environments to assist musicians.

Image-to-Image Translation Music Generation +2

Multilingual Autoregressive Entity Linking

1 code implementation23 Mar 2021 Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni

Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.

Ranked #2 on Entity Disambiguation on Mewsli-9 (using extra training data)

Entity Disambiguation Entity Linking

A Memory Efficient Baseline for Open Domain Question Answering

no code implementations30 Dec 2020 Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Sebastian Riedel, Edouard Grave

Recently, retrieval systems based on dense representations have led to important improvements in open-domain question answering, and related tasks.

Dimensionality Reduction Open-Domain Question Answering +1

Generating Fact Checking Briefs

no code implementations EMNLP 2020 Angela Fan, Aleksandra Piktus, Fabio Petroni, Guillaume Wenzek, Marzieh Saeidi, Andreas Vlachos, Antoine Bordes, Sebastian Riedel

Fact checking at scale is difficult -- while the number of active fact checking websites is growing, it remains too small for the needs of the contemporary media ecosystem.

Fact Checking Question Answering

Video Understanding as Machine Translation

no code implementations12 Jun 2020 Bruno Korbar, Fabio Petroni, Rohit Girdhar, Lorenzo Torresani

With the advent of large-scale multimodal video datasets, especially sequences with audio or transcribed speech, there has been a growing interest in self-supervised learning of video representations.

Machine Translation Metric Learning +5

How Context Affects Language Models' Factual Predictions

no code implementations AKBC 2020 Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel

When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering.

Information Retrieval Language Modelling +2

Scalable Zero-shot Entity Linking with Dense Entity Retrieval

3 code implementations EMNLP 2020 Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, Luke Zettlemoyer

This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off.

Entity Embeddings Entity Linking +2

SAFE: Self-Attentive Function Embeddings for Binary Similarity

3 code implementations13 Nov 2018 Luca Massarelli, Giuseppe Antonio Di Luna, Fabio Petroni, Leonardo Querzoni, Roberto Baldoni

We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions.

Malware Analysis Vulnerability Detection

A Comparison of Two Paraphrase Models for Taxonomy Augmentation

no code implementations NAACL 2018 Vassilis Plachouras, Fabio Petroni, Timothy Nugent, Jochen L. Leidner

Our results show that paraphrasing is a viable method to enrich a taxonomy with more terms, and that Moses consistently outperforms the sequence-to-sequence neural model.

Document Classification Machine Translation +2

attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines

no code implementations NAACL 2018 Fabio Petroni, Vassilis Plachouras, Timothy Nugent, Jochen L. Leidner

Our experimental results on a text classification task demonstrate that using attr2vec to jointly learn embeddings for words and Part-of-Speech (POS) tags improves results compared to learning the embeddings independently.

Dependency Parsing Information Retrieval +4

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