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license: mit
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language:
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pretty_name: LongTVQA+
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---
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# LongTVQA+ Dataset
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This repository contains the **LongTVQA+** dataset in JSON format.
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LongTVQA+ is built upon the original **TVQA+** dataset, with the key difference that it **extends the question grounding scope from short clip-level segments (≈1 minute) to long episode-level videos (up to ~20 minutes)**.
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This enables research on long-form video understanding, long-range temporal reasoning, and fine-grained spatio-temporal grounding in realistic TV show episodes.
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In addition to the extended temporal scope, LongTVQA+ preserves and leverages the rich annotations provided in TVQA+, including:
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1. Frame-level bounding box annotations for visual concept words appearing in questions and correct answers.
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2. Refined timestamp annotations aligned with long episode-level context.
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Please refer to the original **TVQA+ paper** for details on the annotation protocol and baseline evaluations.
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---
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## Files
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- `LongTVQA_plus_train.json` — training split (23,545 QA samples)
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- `LongTVQA_plus_val.json` — validation split (3,017 QA samples)
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- `LongTVQA_plus_subtitle_clip_level.json` — clip-level subtitles indexed by video clip (4,198 clips)
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- `LongTVQA_plus_subtitle_episode_level.json` — episode-level subtitles indexed by episode (220 episodes)
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---
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## QA JSON Format
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Each entry in `LongTVQA_plus_train.json` and `LongTVQA_plus_val.json` is a dictionary with the following fields:
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| Key | Type | Description |
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| --- | --- | --- |
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| `qid` | int | Question ID (same as in TVQA+). |
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| `q` | str | Question text. |
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| `a0` ... `a4` | str | Five multiple-choice answers. |
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| `answer` | str | Correct answer key (`"a0"`–`"a4"`). |
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| `ts` | list | Refined timestamp annotation. For example, `[0, 5.4]` indicates the localized temporal span starts at 0s and ends at 5.4s. |
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| `episode_name` | str | Episode ID (e.g. `s01e02`). |
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| `occur_clip` | str | Video clip name. Format: `{show_name_abbr}_s{season}e{episode}_seg{segment}_clip_{clip}`. Episodes are typically divided into two segments separated by the opening theme. For **The Big Bang Theory**, `{show_name_abbr}` is omitted (e.g. `s05e02_seg02_clip_00`). |
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| `bbox` | dict | Frame-level bounding box annotations sampled at 3 FPS. Keys are frame indices. Values are lists of bounding boxes with `img_id`, `top`, `left`, `width`, `height`, and `label`. |
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---
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### QA Sample
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```json
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{
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"answer": "a1",
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"qid": 134094,
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"ts": [5.99, 11.98],
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"a1": "Howard is talking to Raj and Leonard",
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"a0": "Howard is talking to Bernadette",
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"a3": "Howard is talking to Leonard and Penny",
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"a2": "Howard is talking to Sheldon , and Raj",
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"q": "Who is Howard talking to when he is in the lab room ?",
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"episode_name": "s05e02",
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"occur_clip": "s05e02_seg02_clip_00",
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"a4": "Howard is talking to Penny and Bernadette",
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"bbox": {
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"14": [
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{
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"img_id": 14,
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"top": 153,
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"label": "Howard",
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"width": 180,
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"height": 207,
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"left": 339
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},
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{
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"img_id": 14,
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"top": 6,
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"label": "lab",
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"width": 637,
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"height": 354,
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"left": 3
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}
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],
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"20": [],
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"26": [],
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"32": [],
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"38": []
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}
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}
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```
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---
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## Subtitles JSON Format
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Two subtitle files are provided to support different temporal granularities:
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| File | Key | Type | Description |
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| ------------------------------------------- | -------------- | ---- | ------------------------------------------------------------------------------------------------------------------- |
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| `LongTVQA_plus_subtitle_clip_level.json` | `vid_name` | str | Clip-level subtitle text, with utterances separated by `<eos>`. |
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| `LongTVQA_plus_subtitle_episode_level.json` | `episode_name` | str | Episode-level subtitle text, including clip markers such as `<seg01_clip_00>`, and utterances separated by `<eos>`. |
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---
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### Subtitles Sample
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```json
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{
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"s09e14_seg02_clip_04": "Sheldon : That 's a risk I'm willing to take ! <eos> Amy : Well , this is so nice . <eos> ..."
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}
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```
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---
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---
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license: mit
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language:
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- en
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pretty_name: LongTVQA+
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---
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# LongTVQA+ Dataset
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+
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This repository contains the **LongTVQA+** dataset in JSON format.
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+
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+
LongTVQA+ is built upon the original **TVQA+** dataset, with the key difference that it **extends the question grounding scope from short clip-level segments (≈1 minute) to long episode-level videos (up to ~20 minutes)**.
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+
This enables research on long-form video understanding, long-range temporal reasoning, and fine-grained spatio-temporal grounding in realistic TV show episodes.
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+
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+
In addition to the extended temporal scope, LongTVQA+ preserves and leverages the rich annotations provided in TVQA+, including:
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+
|
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+
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1. Frame-level bounding box annotations for visual concept words appearing in questions and correct answers.
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+
2. Refined timestamp annotations aligned with long episode-level context.
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+
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Please refer to the original **TVQA+ paper** for details on the annotation protocol and baseline evaluations.
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+
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---
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## Files
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- `LongTVQA_plus_train.json` — training split (23,545 QA samples)
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- `LongTVQA_plus_val.json` — validation split (3,017 QA samples)
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- `LongTVQA_plus_subtitle_clip_level.json` — clip-level subtitles indexed by video clip (4,198 clips)
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- `LongTVQA_plus_subtitle_episode_level.json` — episode-level subtitles indexed by episode (220 episodes)
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---
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## QA JSON Format
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Each entry in `LongTVQA_plus_train.json` and `LongTVQA_plus_val.json` is a dictionary with the following fields:
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| Key | Type | Description |
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| --- | --- | --- |
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| `qid` | int | Question ID (same as in TVQA+). |
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| `q` | str | Question text. |
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| `a0` ... `a4` | str | Five multiple-choice answers. |
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| `answer` | str | Correct answer key (`"a0"`–`"a4"`). |
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| `ts` | list | Refined timestamp annotation. For example, `[0, 5.4]` indicates the localized temporal span starts at 0s and ends at 5.4s. |
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| `episode_name` | str | Episode ID (e.g. `s01e02`). |
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| `occur_clip` | str | Video clip name. Format: `{show_name_abbr}_s{season}e{episode}_seg{segment}_clip_{clip}`. Episodes are typically divided into two segments separated by the opening theme. For **The Big Bang Theory**, `{show_name_abbr}` is omitted (e.g. `s05e02_seg02_clip_00`). |
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| `bbox` | dict | Frame-level bounding box annotations sampled at 3 FPS. Keys are frame indices. Values are lists of bounding boxes with `img_id`, `top`, `left`, `width`, `height`, and `label`. |
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+
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---
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+
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### QA Sample
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```json
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{
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"answer": "a1",
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"qid": 134094,
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"ts": [5.99, 11.98],
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"a1": "Howard is talking to Raj and Leonard",
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"a0": "Howard is talking to Bernadette",
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"a3": "Howard is talking to Leonard and Penny",
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"a2": "Howard is talking to Sheldon , and Raj",
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"q": "Who is Howard talking to when he is in the lab room ?",
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"episode_name": "s05e02",
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"occur_clip": "s05e02_seg02_clip_00",
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"a4": "Howard is talking to Penny and Bernadette",
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"bbox": {
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"14": [
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{
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"img_id": 14,
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"top": 153,
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"label": "Howard",
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"width": 180,
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"height": 207,
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"left": 339
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},
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{
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"img_id": 14,
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"top": 6,
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"label": "lab",
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"width": 637,
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"height": 354,
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"left": 3
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}
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],
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"20": [],
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"26": [],
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"32": [],
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"38": []
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}
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}
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```
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---
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## Subtitles JSON Format
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Two subtitle files are provided to support different temporal granularities:
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| File | Key | Type | Description |
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| ------------------------------------------- | -------------- | ---- | ------------------------------------------------------------------------------------------------------------------- |
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| `LongTVQA_plus_subtitle_clip_level.json` | `vid_name` | str | Clip-level subtitle text, with utterances separated by `<eos>`. |
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| `LongTVQA_plus_subtitle_episode_level.json` | `episode_name` | str | Episode-level subtitle text, including clip markers such as `<seg01_clip_00>`, and utterances separated by `<eos>`. |
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---
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### Subtitles Sample
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```json
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{
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"s09e14_seg02_clip_04": "Sheldon : That 's a risk I'm willing to take ! <eos> Amy : Well , this is so nice . <eos> ..."
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}
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```
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---
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## 📝 Citation
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If you find our work helpful, please cite:
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```bibtex
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@misc{liu2025longvideoagentmultiagentreasoninglong,
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title={LongVideoAgent: Multi-Agent Reasoning with Long Videos},
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author={Runtao Liu and Ziyi Liu and Jiaqi Tang and Yue Ma and Renjie Pi and Jipeng Zhang and Qifeng Chen},
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year={2025},
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eprint={2512.20618},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={[https://arxiv.org/abs/2512.20618](https://arxiv.org/abs/2512.20618)},
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}
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## License
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This dataset is released under the **MIT License**.
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