Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics |
| Place of Publication | Florence, Italy |
| Publisher | Association for Computational Linguistics |
| Pages | 6452-6462 |
| Number of pages | 11 |
| Publication status | Published - 1 Jul 2019 |
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Symbolic Inductive Bias for Visually Grounded Learning of Spoken Language. / Chrupala, Grzegorz.
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy : Association for Computational Linguistics, 2019. p. 6452-6462.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
TY - GEN
T1 - Symbolic Inductive Bias for Visually Grounded Learning of Spoken Language
AU - Chrupala, Grzegorz
PY - 2019/7/1
Y1 - 2019/7/1
N2 - A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.
AB - A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.
M3 - Conference contribution
SP - 6452
EP - 6462
BT - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
PB - Association for Computational Linguistics
CY - Florence, Italy
ER -