Spence Green

Posts by Spence Green:

Announcing Lilt's Series A Financing

Today I’m pleased to announce that we raised $9.5M in new funding led by Sequoia Capital. Bill Coughran, partner at Sequoia, will join our board. Our existing investors‒Redpoint Ventures, Zetta Venture Partners, and XSeed Capital‒all participated in the round.

Series A funding indicates two milestones in an enterprise […]

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What We’re Reading: Domain Attention with an Ensemble of Experts

A major problem in effective deployment of machine learning systems in practice is domain adaptation — given a large auxiliary supervised dataset and a smaller dataset of interest, using the auxiliary dataset to increase performance on the smaller dataset. This paper considers the case where we have K datasets from distinct […]

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What We’re Reading: Learning to Decode for Future Success

When doing beam search in sequence to sequence models, one explores next words in order of their likelihood. However, during decoding, there may be other constraints we have or objectives we wish to maximize. For example, sequence length, BLEU score, or mutual information between the target and source sentences. In order to […]

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What We’re Reading: Neural Machine Translation with Reconstruction

Neural MT systems generate translations one word at a time. They can still generate fluid translations because they choose each word based on all of the words generated so far. Typically, these systems are just trained to generate the next word correctly, based on all previous words. One systematic problem with this […]

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What We’re Reading: Single-Queue Decoding for Neural Machine Translation

The most popular way of finding a translation for a source sentence with a neural sequence-to-sequence model is a simple beam search. The target sentence is predicted one word at a time and after each prediction, a fixed number of possibilities (typically between 4 and 10) is retained for further exploration. This strategy can […]

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Technology for Interactive MT

This article describes the technology behind Lilt’s interactive translation suggestions. The details were first published in an academic conference paper, Models and Inference for Prefix-Constrained Machine Translation.

Machine translation systems can translate whole sentences or documents, but they can also be used to […]

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