Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 93 tok/s
Gemini 2.5 Pro 53 tok/s Pro
GPT-5 Medium 32 tok/s
GPT-5 High 30 tok/s Pro
GPT-4o 97 tok/s
GPT OSS 120B 473 tok/s Pro
Kimi K2 228 tok/s Pro
2000 character limit reached

Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models (1911.03782v1)

Published 9 Nov 2019 in cs.CL and cs.LG

Abstract: Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability. We discretize the encoder output units into a predefined interpretable vocabulary space using the Connectionist Temporal Classification (CTC) loss. Our modular systems achieve near SOTA performance on the 300h Switchboard benchmark, with WER of 8.3% and 17.6% on the SWB and CH subsets, using seq2seq models with encoder and decoder modules which are independent and interchangeable.

Citations (10)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube