RRepoGEO

REPOGEO REPORT · LITE

THUNLP-MT/MT-Reading-List

Default branch master · commit b79517bc · scanned 5/26/2026, 8:57:52 AM

GitHub: 2,445 stars · 442 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface THUNLP-MT/MT-Reading-List, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to emphasize curated list value

    Why:

    CURRENT
    This is a machine translation reading list maintained by the Tsinghua Natural Language Processing Group.
    COPY-PASTE FIX
    This is the definitive, actively maintained machine translation reading list curated by the Tsinghua Natural Language Processing Group, providing a structured guide to essential papers for researchers and students in the field.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/THUNLP-MT/MT-Reading-List
  • lowtopics#3
    Add `neural-machine-translation` to repository topics

    Why:

    CURRENT
    ["machine-translation", "reading-list"]
    COPY-PASTE FIX
    ["machine-translation", "reading-list", "neural-machine-translation"]

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface THUNLP-MT/MT-Reading-List
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ACL Anthology
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ACL Anthology · recommended 1×
  2. Neural Machine Translation and Sequence-to-sequence Models: A Tutorial by Philipp Koehn · recommended 1×
  3. Statistical Machine Translation by Philipp Koehn · recommended 1×
  4. A Survey of Neural Machine Translation by Zhou et al. · recommended 1×
  5. University of Edinburgh's MT Course · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of essential papers for understanding machine translation?
    you: not recommended
    AI recommended (in order):
    1. ACL Anthology
    2. Neural Machine Translation and Sequence-to-sequence Models: A Tutorial by Philipp Koehn
    3. Statistical Machine Translation by Philipp Koehn
    4. A Survey of Neural Machine Translation by Zhou et al.
    5. University of Edinburgh's MT Course
    6. Stanford University's CS224N
    7. Carnegie Mellon University (CMU) MT Courses
    8. Awesome Machine Translation GitHub Repositories
    9. Google Scholar

    AI recommended 9 alternatives but never named THUNLP-MT/MT-Reading-List. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the most important recent research papers in neural machine translation?
    you: not recommended
    AI recommended (in order):
    1. Transformer architecture
    2. BERT
    3. BART
    4. XLM-RoBERTa

    AI recommended 4 alternatives but never named THUNLP-MT/MT-Reading-List. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of THUNLP-MT/MT-Reading-List?
    pass
    AI did not name THUNLP-MT/MT-Reading-List — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts THUNLP-MT/MT-Reading-List in production, what risks or prerequisites should they evaluate first?
    pass
    AI named THUNLP-MT/MT-Reading-List explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo THUNLP-MT/MT-Reading-List solve, and who is the primary audience?
    pass
    AI did not name THUNLP-MT/MT-Reading-List — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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THUNLP-MT/MT-Reading-List — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite