How does the model improve the process of creating parallel corpora?

The model improves the process of creating parallel corpora by automatically aligning subtitles to sign language videos, making it easier to create large-scale parallel corpora for translation tasks.

What type of model is used in the video?

The video uses a Transformer-based model to align subtitles to sign language videos.

What does the model use as cues for alignment?

The model uses semantic cues from subtitle text content for alignment.

What is the goal of aligning subtitles to sign language videos?

The goal of aligning subtitles to sign language videos is to improve the process of creating large-scale parallel corpora for translation tasks.

Aligning Subtitles in Sign Language Video | ICCV'21

Name: Aligning Subtitles in Sign Language Video | ICCV'21
Uploaded: 2023-07-31 13:56:29
Description: Develop a Transformer-based model that uses semantic cues from subtitle text content to automatically align subtitles to sign language videos, improving the process of creating large-scale parallel corpora for translation tasks.

This article is a summary of a YouTube video "[ICCV'21] Aligning Subtitles in Sign Language Video" by Hannah Bull

TLDR Develop a Transformer-based model that uses semantic cues from subtitle text content to automatically align subtitles to sign language videos, improving the process of creating large-scale parallel corpora for translation tasks.

Key insights

🌐

Automatic alignment of subtitles to sign language videos is crucial for creating large-scale parallel corpora for translation tasks, providing accessibility and inclusivity for the deaf community.
💡

The aim of this work is to temporally locate subtitles in sign language videos, rather than individual signs.
🧠

Our work builds upon previous research by incorporating semantic cues from subtitle text content, enhancing the segmentation of sign language into subtitle-units.
📝

Sign language subtitles are not a direct transcription of the audio content, but rather a translation, highlighting the lack of a one-to-one relationship between words in the subtitle text and signs in the signing segment.
⚖️

The proposed Transformer-based model inverts the conventional video-text Transformers, feeding the video into the "Decoder" and the subtitle text into the "Encoder," challenging the traditional approach.
⏱️

The use of Dynamic Time Warping in the second stage helps resolve conflicts over a long video, ensuring accurate alignment of subtitles in sign language videos.
📊

Using sign spottings as a baseline method for aligning subtitles is not effective, as only 15% of the subtitles in the test set can be confidently associated with a sign spotting, highlighting the insufficiency of relying solely on sign localization for subtitle alignment.
🌍

The goal is to obtain video-subtitle pairs that can train unconstrained machine translation systems for sign languages, but it requires a written translation of sign language video and approximate alignments.

Q&A

What is the main idea of the video?

— The main idea of the video is to develop a Transformer-based model that uses semantic cues from subtitle text content to automatically align subtitles to sign language videos.
How does the model improve the process of creating parallel corpora?

— The model improves the process of creating parallel corpora by automatically aligning subtitles to sign language videos, making it easier to create large-scale parallel corpora for translation tasks.
What type of model is used in the video?

— The video uses a Transformer-based model to align subtitles to sign language videos.
What does the model use as cues for alignment?

— The model uses semantic cues from subtitle text content for alignment.
What is the goal of aligning subtitles to sign language videos?

— The goal of aligning subtitles to sign language videos is to improve the process of creating large-scale parallel corpora for translation tasks.

Timestamped Summary

📝

00:00
Aligning subtitles to continuous sign language in interpreted videos is crucial for creating large-scale parallel corpora for translation tasks and requires automatic alignment at a sentence-like level.
📺

00:34
Subtitles in sign language videos can be temporally located using visual dictionaries, allowing for continuous translation in constrained settings with limited vocabulary.
📝

01:10
Our work improves upon a previous study by incorporating semantic cues from subtitle text content to segment sign language into subtitle-units, using manually aligned subtitles to the signing for fully supervised training.
📺

01:42
Automatically aligning subtitles to sign language videos is challenging due to variable lag between audio and signing, differences in duration between spoken and signed phrases, and the lack of a one-to-one relationship between subtitle text and signs.
🤖

02:29
A Transformer-based model predicts the alignment between subtitle text and a sign language video sequence by feeding the video into the "Decoder" and the subtitle text into the "Encoder", providing an approximate prior location of the signing-aligned subtitles.
💡

03:08
Predicted subtitles in sign language videos may overlap in time, so a Dynamic Time Warping technique is used to resolve conflicts in long videos.
📊

03:22
Shifting audio subtitles back by 3 seconds improves the low performance of audio-aligned subtitles, outperforming sign spotting methods for subtitle alignment.
📝

03:56
Our SAT model improves upon previous baselines by aligning subtitles using both text content and prosodic cues, with the goal of training machine translation systems for sign languages, although it requires written translations and approximate alignments, and the style of BSL interpreted from English may differ from original BSL content.