Sumit Chopra

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 …

A Neural Attention Model for Abstractive Sentence Summarization

2015

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local …

Sequence Level Training with Recurrent Neural Networks

2015 · arXiv (Cornell University)

Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. …

A Neural Attention Model for Sentence Summarization

2015

Summarization based on text extraction is inherently limited, but generation-style ab-stractive methods have proven challeng-ing to build. In this work, we propose a fully data-driven approach to abstrac-tive sentence summarization. Our method utilizes a local …

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 …

Sequence Level Training with Recurrent Neural Networks

2016 · International Conference on Learning Representations

Abstract: Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an …

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; …