kawin

5 ms·

1. 1.▲
   by kawin 2y ago

   This is great advice! I'd like to add that if you don't have pairwise preference data (A > B) but do have binary data (A is good for x_1, B is good for x_2, etc.), then Kahneman-Tversky Optimization (KTO) might be a better fit.
2. 2.▲
   The Problem with NLP Leaderboards(arxiv.org)

   4 points by kawin 6y ago|0 comments
3. 3.▲
   A Critique of Leaderboard Culture in AI(twitter.com)

   3 points by kawin 6y ago|0 comments
4. 4.▲
   by kawin 8y ago

   That's a fair point. I was initially thinking of targeting businesses rather than individual consumers, so I thought a flexible pricing plan would be helpful (given variations in headcount, sector, etc.). It looks like individual consu
5. 5.▲
   by kawin 8y ago

   This is a fascinating idea - it's definitely possible, though easier for some groups of people than others (I don't think I'll be able to hire doctors for $25/article). I think the human and AI analyses would contrast ea
6. 6.▲
   by kawin 8y ago

   Thanks for the feedback! User privacy is definitely important to me, especially since users might be entering recruiting / product-related data into the app. The text that you analyze is not stored on any Toasted database (the most rec
7. 7.▲
   by kawin 8y ago

   Thanks for taking the time to write this out! I agree on the UX suggestions. The suggestions for each word are a list of terms that could work, with the score being conditional on the cohort selected. The darker the alternative, the worse i
8. 8.▲
   by kawin 8y ago

   Got it. The system does use a state-of-the-art POS tagger, but I think 'key' was mistakenly tagged as an adjective instead of as a noun for your sentence. I'll try to fix that -- thank for pointing it out!
9. 9.▲
   by kawin 8y ago

   Textio looks like a a great product, but IIUC its use case is very specific -- how can you make your job description more enticing and gender-neutral? Toasted is meant to be more of a big tent product: you're not just working with men&
10. 10.▲
    by kawin 8y ago

    Glad you like the idea! At the moment, the model requires a fairly large amount of data, which is why the app only offers large well-defined cohorts to select from (e.g., accountants, retirees). I like your idea though, and it may be possib
11. 11.▲
    by kawin 8y ago

    Thanks for trying it out! The possible changes you can make stays the same across different cohorts of people, but the score assigned to the word changes (e.g., 'security' has a higher score for retirees than college students). Th
12. 12.▲
    Show HN: Predict how well people will react to your writing(isittoasted.com)

    84 points by kawin 8y ago|50 comments
13. 13.▲
    by kawin 8y ago

    Interesting question! I think you have the right idea: the GloVe or SGNS vector for a word is some composition of the word sense representations. The number of senses for a word isn't necessarily finite either -- one could argue that e
14. 14.▲
    by kawin 8y ago

    Hi, first author here! Feel free to ask any questions. TL;DR: We prove that linear word analogies hold over a set of ordered pairs (e.g., {(Paris, France), (Ottawa, Canada), ...}) in an SGNS or GloVe embedding space with no reconstruction e
15. 15.▲
    Towards Understanding Linear Word Analogies(arxiv.org)

    70 points by kawin 8y ago|9 comments
16. 16.▲
    Show HN: Toasted – How gender-specific is the tone of your writing?(isittoasted.herokuapp.com)

    6 points by kawin 8y ago|0 comments
17. 17.▲
    by kawin 8y ago

    Thanks for taking a look! "Feeling" and "feel" are both pretty close to neutral, but it looks like the different in verb tense is responsible for them being on different halves of the spectrum.
18. 18.▲
    Show HN: Toasted – Remove gender bias from your writing

    5 points by kawin 8y ago|3 comments