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MATINF - Multitask Chinese NLP Dataset

The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".

Citation

If you use the dataset or code in your research, please kindly cite our work:

@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}

Dataset

You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form. ALL USE MUST BE NON-COMMERCIAL!!

Code

Please manually change the stage variable in main() to toggle from different training phases.

Then run:

python run.py

Code credit: Hongtao Wu

About

The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".

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43 stars

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3 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
MATINF - Multitask Chinese NLP Dataset
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Citation
If you use the dataset or code in your research, please kindly cite our work:
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
Dataset
You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form.
ALL USE MUST BE NON-COMMERCIAL!!
Code
Please manually change the stage variable in main() to toggle from different training phases.
Then run:
python run.py
Code credit: Hongtao Wu
About
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Resources
Readme
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43 stars
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3 watching
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8 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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run.py
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seq2seq_model.py
utils.py
utils.py
vocab.py
vocab.py
Repository files navigation
MATINF - Multitask Chinese NLP Dataset
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Citation
If you use the dataset or code in your research, please kindly cite our work:
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
Dataset
You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form.
ALL USE MUST BE NON-COMMERCIAL!!
Code
Please manually change the stage variable in main() to toggle from different training phases.
Then run:
python run.py
Code credit: Hongtao Wu
About
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Resources
Readme
Activity
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Stars
43 stars
Watchers
3 watching
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8 forks
Report repository
Releases
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Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
MATINF - Multitask Chinese NLP Dataset
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Citation
If you use the dataset or code in your research, please kindly cite our work:
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
Dataset
You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form.
ALL USE MUST BE NON-COMMERCIAL!!
Code
Please manually change the stage variable in main() to toggle from different training phases.
Then run:
python run.py
Code credit: Hongtao Wu
About
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Resources
Readme
Activity
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Stars
43 stars
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3 watching
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8 forks
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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run.py
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utils.py
utils.py
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vocab.py
Repository files navigation
MATINF - Multitask Chinese NLP Dataset
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Citation
If you use the dataset or code in your research, please kindly cite our work:
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
Dataset
You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form.
ALL USE MUST BE NON-COMMERCIAL!!
Code
Please manually change the stage variable in main() to toggle from different training phases.
Then run:
python run.py
Code credit: Hongtao Wu
About
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Resources
Readme
Activity
Custom properties
Stars
43 stars
Watchers
3 watching
Forks
8 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
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run.py
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utils.py
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vocab.py
Repository files navigation
MATINF - Multitask Chinese NLP Dataset
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Citation
If you use the dataset or code in your research, please kindly cite our work:
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
Dataset
You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form.
ALL USE MUST BE NON-COMMERCIAL!!
Code
Please manually change the stage variable in main() to toggle from different training phases.
Then run:
python run.py
Code credit: Hongtao Wu
About
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Resources
Readme
Activity
Custom properties
Stars
43 stars
Watchers
3 watching
Forks
8 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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run.py
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seq2seq_model.py
utils.py
utils.py
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vocab.py
Repository files navigation
MATINF - Multitask Chinese NLP Dataset
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Citation
If you use the dataset or code in your research, please kindly cite our work:
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
Dataset
You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form.
ALL USE MUST BE NON-COMMERCIAL!!
Code
Please manually change the stage variable in main() to toggle from different training phases.
Then run:
python run.py
Code credit: Hongtao Wu
About
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Resources
Readme
Activity
Custom properties
Stars
43 stars
Watchers
3 watching
Forks
8 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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run.py
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utils.py
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vocab.py
Repository files navigation
MATINF - Multitask Chinese NLP Dataset
The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
Citation
If you use the dataset or code in your research, please kindly cite our work:
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
Dataset
You can get MATINF dataset by signing the agreement on Google Form to request the access. You will get the download link and the zip password after filling the form.
ALL USE MUST BE NON-COMMERCIAL!!
Code
Please manually change the stage variable in main() to toggle from different training phases.
Then run:
python run.py
Code credit: Hongtao Wu
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The dataset and PyTorch Implementation for ACL 2020 paper "MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization".
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