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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Transcribing Natural Languages for the Deaf via Neural Editing Programs

February 1, 2023

Authors

Dongxu Li

The Australian National University Data61/CSIRO


Chenchen Xu

The Australian National University Data61/CSIRO


Liu Liu

Huawei Cyberverse Lab


Yiran Zhong

SenseTime


Rong Wang

The Australian National University


Lars Petersson

The Australian National University Data61/CSIRO


Hongdong Li

Australian National University


Proceedings:

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Special Track on AI for Social Impact

Downloads:

Download PDF

Abstract:

This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign language glosses. Previous sequence-to-sequence language models trained with paired sentence-gloss data often fail to capture the rich connections between the two distinct languages, leading to unsatisfactory transcriptions. We observe that despite different grammars, glosses effectively simplify sentences for the ease of deaf communication, while sharing a large portion of vocabulary with sentences. This has motivated us to implement glossification by executing a collection of editing actions, e.g. word addition, deletion, and copying, called editing programs, on their natural spoken language counterparts. Specifically, we design a new neural agent that learns to synthesize and execute editing programs, conditioned on sentence contexts and partial editing results. The agent is trained to imitate minimal editing programs, while exploring more widely the program space via policy gradients to optimize sequence-wise transcription quality. Results show that our approach outperforms previous glossification models by a large margin, improving the BLEU-4 score from 16.45 to 18.89 on RWTH-PHOENIX-WEATHER-2014T and from 18.38 to 21.30 on CSL-Daily.

DOI:

10.1609/aaai.v36i11.21457


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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