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The Latest and Greatest from Salesforce Research

Learning without Labels

With data rapidly being generated by millions of people, it's not feasible to label all of it. Learn about the recent advancements in ML for how to train vision models with unlabelled data using self-supervised learning.

21 Jun 2021 • Michael Sollami #deeplearning

Salesforce Research at EMNLP 2020

This year marks the 24th annual Empirical Methods in Natural Language Processing (EMNLP) conference reimagined for the first time ever in a fully virtual format. EMNLP is a leading conference in the area of Natural Language Processing covering a broad spectrum of diverse research areas that are concerned with computational

11 Nov 2020 • Denna Mafie #research

Explaining Solutions to Physical Reasoning Tasks

We show that deep neural models can describe common sense physics in a valid and sufficient way that is also generalizable. Our ESPRIT framework is trained on a new dataset with physics simulations and descriptions that we collected and have open-sourced.

05 May 2020 • Nazneen Rajani #research

ERASER: A Benchmark to Evaluate Rationalized NLP Models

Many NLP applications today deploy state-of-the-art deep neural networks that are essentially black-boxes. One of the goals of Explainable AI (XAI) is to have AI models reveal why and how they make their predictions so that these predictions are interpretable by a human. But work in this direction has been

08 Nov 2019 • Nazneen Rajani #research

Ethics in AI research papers and articles

This is my obsessively curated list of research papers and articles on ethics in AI that I have been collecting over the years. Ones in bold are those that I refer back to and found particularly useful. Let me know if I am missing your favorites.

20 Jan 2019 • Kathy Baxter #ethics

The Natural Language Decathlon

Deep learning has significantly improved state-of-the-art performance for natural language processing tasks like machine translation, summarization, question answering, and text classification.

20 Jun 2018 • Bryan McCann #research

Interpretable Counting for Visual Question Answering

Learning to answer open-ended questions about images, a task known as visual question answering (VQA), has received much attention over the last several years. VQA has been put forth as a benchmark for complete scene understanding and flexible reasoning, two fundamental goals of AI.

14 Dec 2017 • Alex Trott #research

Improving end-to-end Speech Recognition Models

Speech recognition has been successfully depolyed on various smart devices, and is changing the way we interact with them. Traditional phonetic-based recognition approaches require training of separate components such as pronouciation, acoustic and language model.

14 Dec 2017 • Yingbo Zhou #research

How to Talk to Your Database

A vast amount of today’s information is stored in relational databases. These databases provide the foundation of systems such as medical records, financial markets, and electronic commerce.

29 Aug 2017 • Victor Zhong #research

Learned in Translation: Contextualized Word Vectors

There are times when word vectors are initialized to lists of random numbers before a model is trained for a specific task, but it is also quite common to initialize the word vectors of a model with those obtained by running methods like word2vec, GloVe, or FastText.

31 Jul 2017 • Bryan McCann #research