Search Results for author: Arvind Neelakantan

Found 21 papers, 9 papers with code

On Task-Level Dialogue Composition of Generative Transformer Model

1 code implementation EMNLP (insights) 2020 Prasanna Parthasarathi, Arvind Neelakantan, Sharan Narang

In this work, we begin by studying the effect of training human-human task-oriented dialogues towards improving the ability to compose multiple tasks on Transformer generative models.

Response Generation Task-Oriented Dialogue Systems

Trading Off Diversity and Quality in Natural Language Generation

no code implementations EACL (HumEval) 2021 Hugh Zhang, Daniel Duckworth, Daphne Ippolito, Arvind Neelakantan

For open-ended language generation tasks such as storytelling and dialogue, choosing the right decoding algorithm is critical to controlling the tradeoff between generation quality and diversity.

Text Generation

Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning

1 code implementation31 Oct 2019 Arvind Neelakantan, Semih Yavuz, Sharan Narang, Vishaal Prasad, Ben Goodrich, Daniel Duckworth, Chinnadhurai Sankar, Xifeng Yan

In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.

Response Generation Text Generation

Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset

1 code implementation IJCNLP 2019 Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Andy Cedilnik, Kyu-Young Kim

A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data.

Parallel Scheduled Sampling

no code implementations11 Jun 2019 Daniel Duckworth, Arvind Neelakantan, Ben Goodrich, Lukasz Kaiser, Samy Bengio

Experimentally, we find the proposed technique leads to equivalent or better performance on image generation, summarization, dialog generation, and translation compared to teacher-forced training.

Image Generation Response Generation

Towards a better understanding of Vector Quantized Autoencoders

no code implementations ICLR 2019 Aurko Roy, Ashish Vaswani, Niki Parmar, Arvind Neelakantan

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.

Knowledge Distillation Machine Translation +1

Theory and Experiments on Vector Quantized Autoencoders

2 code implementations28 May 2018 Aurko Roy, Ashish Vaswani, Arvind Neelakantan, Niki Parmar

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.

Image Generation Knowledge Distillation +2

Learning a Natural Language Interface with Neural Programmer

2 code implementations28 Nov 2016 Arvind Neelakantan, Quoc V. Le, Martin Abadi, Andrew McCallum, Dario Amodei

The main experimental result in this paper is that a single Neural Programmer model achieves 34. 2% accuracy using only 10, 000 examples with weak supervision.

Natural Language Queries Program induction +1

Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

2 code implementations EACL 2017 Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum

Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks.

Logical Reasoning

Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

1 code implementation EACL 2017 Patrick Verga, Arvind Neelakantan, Andrew McCallum

In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time.

Matrix Completion

Adding Gradient Noise Improves Learning for Very Deep Networks

4 code implementations21 Nov 2015 Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens

This success is partially attributed to architectural innovations such as convolutional and long short-term memory networks.

Question Answering

Neural Programmer: Inducing Latent Programs with Gradient Descent

no code implementations16 Nov 2015 Arvind Neelakantan, Quoc V. Le, Ilya Sutskever

In this work, we propose Neural Programmer, an end-to-end differentiable neural network augmented with a small set of basic arithmetic and logic operations.

Question Answering speech-recognition +1

Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space

no code implementations EMNLP 2014 Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, Andrew McCallum

There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale.

Word Embeddings Word Similarity

Compositional Vector Space Models for Knowledge Base Completion

no code implementations IJCNLP 2015 Arvind Neelakantan, Benjamin Roth, Andrew McCallum

Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X, Y) from bornIn(X, Y).

Knowledge Base Completion Zero-Shot Learning

Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods

no code implementations HLT 2015 Arvind Neelakantan, Ming-Wei Chang

In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention.

Knowledge Base Completion Relation Extraction

Learning Dictionaries for Named Entity Recognition using Minimal Supervision

no code implementations EACL 2014 Arvind Neelakantan, Michael Collins

This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples.

named-entity-recognition NER +1

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