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.
no code implementations • ACL 2020 • Kurt Shuster, Samuel Humeau, Antoine Bordes, Jason Weston
To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019).
no code implementations • 22 Jun 2020 • Stephen Roller, Y-Lan Boureau, Jason Weston, Antoine Bordes, Emily Dinan, Angela Fan, David Gunning, Da Ju, Margaret Li, Spencer Poff, Pratik Ringshia, Kurt Shuster, Eric Michael Smith, Arthur Szlam, Jack Urbanek, Mary Williamson
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.
1 code implementation • LREC 2022 • Louis Martin, Angela Fan, Éric de la Clergerie, Antoine Bordes, Benoît Sagot
Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English.
Ranked #2 on Text Simplification on ASSET
1 code implementation • ACL 2020 • Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, Benoît Sagot, Lucia Specia
Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
no code implementations • 27 Apr 2020 • Angela Fan, Claire Gardent, Chloe Braud, Antoine Bordes
Various machine learning tasks can benefit from access to external information of different modalities, such as text and images.
1 code implementation • IJCNLP 2019 • Angela Fan, Claire Gardent, Chloe Braud, Antoine Bordes
Query-based open-domain NLP tasks require information synthesis from long and diverse web results.
Ranked #4 on Open-Domain Question Answering on ELI5
2 code implementations • LREC 2020 • Louis Martin, Benoît Sagot, Éric de la Clergerie, Antoine Bordes
Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical.
Ranked #3 on Text Simplification on ASSET
no code implementations • 25 Sep 2019 • Angela Fan, Claire Gardent, Chloe Braud, Antoine Bordes
Various machine learning tasks can benefit from access to external information of different modalities, such as text and images.
1 code implementation • WS 2018 • Louis Martin, Samuel Humeau, Pierre-Emmanuel Mazaré, Antoine Bordes, Éric Villemonte de la Clergerie, Benoît Sagot
We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.
1 code implementation • ACL 2019 • Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazaré, Jason Weston
As our agent engages in conversation, it also estimates user satisfaction in its responses.
3 code implementations • 2 Nov 2018 • Kurt Shuster, Samuel Humeau, Antoine Bordes, Jason Weston
To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019).
Ranked #2 on Text Retrieval on Image-Chat
no code implementations • CVPR 2019 • Kurt Shuster, Samuel Humeau, Hexiang Hu, Antoine Bordes, Jason Weston
While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions.
1 code implementation • EMNLP 2018 • Pierre-Emmanuel Mazaré, Samuel Humeau, Martin Raison, Antoine Bordes
Current dialogue systems are not very engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies.
no code implementations • 27 Apr 2018 • Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes
This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them.
1 code implementation • 3 Apr 2018 • Othman Sbai, Mohamed Elhoseiny, Antoine Bordes, Yann Lecun, Camille Couprie
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant?
no code implementations • NeurIPS 2017 • Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space.
3 code implementations • 12 Sep 2017 • Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
3 code implementations • 1 Jun 2017 • Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space.
22 code implementations • EMNLP 2017 • Alexander H. Miller, Will Feng, Adam Fisch, Jiasen Lu, Dhruv Batra, Antoine Bordes, Devi Parikh, Jason Weston
We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl. ai.
22 code implementations • EMNLP 2017 • Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, Antoine Bordes
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
Ranked #1 on Semantic Textual Similarity on SentEval
Cross-Lingual Natural Language Inference Semantic Textual Similarity +3
10 code implementations • ACL 2017 • Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.
Ranked #1 on Open-Domain Question Answering on SQuAD1.1
5 code implementations • 12 Dec 2016 • Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann Lecun
The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting.
Ranked #5 on Procedural Text Understanding on ProPara
2 code implementations • EMNLP 2016 • Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, Jason Weston
Directly reading documents and being able to answer questions from them is an unsolved challenge.
Ranked #12 on Question Answering on WikiQA
6 code implementations • 24 May 2016 • Antoine Bordes, Y-Lan Boureau, Jason Weston
We show similar result patterns on data extracted from an online concierge service.
1 code implementation • 21 Nov 2015 • Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, Jason Weston
A long-term goal of machine learning is to build intelligent conversational agents.
3 code implementations • 7 Nov 2015 • Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston
We introduce a new test of how well language models capture meaning in children's books.
3 code implementations • 5 Jun 2015 • Antoine Bordes, Nicolas Usunier, Sumit Chopra, Jason Weston
Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions.
Ranked #1 on Question Answering on WebQuestions (F1 metric)
2 code implementations • 2 Jun 2015 • Alberto Garcia-Duran, Antoine Bordes, Nicolas Usunier, Yves GRANDVALET
This paper tackles the problem of endogenous link prediction for Knowledge Base completion.
20 code implementations • 19 Feb 2015 • Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent.
5 code implementations • 15 Oct 2014 • Jason Weston, Sumit Chopra, Antoine Bordes
We describe a new class of learning models called memory networks.
1 code implementation • EMNLP 2014 • Antoine Bordes, Sumit Chopra, Jason Weston
Training our system using pairs of questions and structured representations of their answers, and pairs of question paraphrases, yields competitive results on a competitive benchmark of the literature.
Ranked #2 on Question Answering on WebQuestions (F1 metric)
no code implementations • 16 Apr 2014 • Antoine Bordes, Jason Weston, Nicolas Usunier
Building computers able to answer questions on any subject is a long standing goal of artificial intelligence.
Ranked #1 on Question Answering on Reverb
8 code implementations • NeurIPS 2013 • Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
Ranked #5 on Link Prediction on FB122
no code implementations • EMNLP 2013 • Jason Weston, Antoine Bordes, Oksana Yakhnenko, Nicolas Usunier
This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge.
no code implementations • 26 Apr 2013 • Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
We consider the problem of embedding entities and relations of knowledge bases in low-dimensional vector spaces.
no code implementations • 15 Jan 2013 • Xavier Glorot, Antoine Bordes, Jason Weston, Yoshua Bengio
Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing.
no code implementations • NeurIPS 2012 • Rodolphe Jenatton, Nicolas L. Roux, Antoine Bordes, Guillaume R. Obozinski
While there is a large body of work focused on modeling these data, few considered modeling these multiple types of relationships jointly.