Antoine Bordes

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

2015 · arXiv (Cornell University)

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. To measure progress towards that goal, we argue for …

Key-Value Memory Networks for Directly Reading Documents

2016

Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven …

Tracking the World State with Recurrent Entity Networks

2016 · arXiv (Cornell University)

We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it …

Reading Wikipedia to Answer Open-Domain Questions

2017 · arXiv (Cornell University)

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. This task of machine reading …

Training Millions of Personalized Dialogue Agents

2018

Current dialogue systems fail at being engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when …

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

2016 · International Conference on Learning Representations

Abstract: 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. To measure progress towards that goal, we argue …

Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data

2017 · arXiv (Cornell University)

Many modern NLP systems rely on word embeddings, previously trained in an\nunsupervised manner on large corpora, as base features. Efforts to obtain\nembeddings for larger chunks of text, such as sentences, have however not been\nso successful. …

The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations

2016 · arXiv (Cornell University)

Abstract: We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lower-frequency …

Large-scale Simple Question Answering with Memory Networks

2015 · arXiv (Cornell University)

Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitask and transfer learning for simple question answering; …