Looking at the paper here: https://arxiv.org/abs/1610.00883

Can anyone tell me what the P R and F "features" mean in table 2?

They use F-score ( https://en.wikipedia.org/wiki/F1_score ) which doesn't take into account true negatives at all. Maybe not an issue since they are evaluating the same set of GoodReads quotes across all four.

The unique features contribute to an F-Score improvement of around 1% with baselines in the 50-80% range. That doesn't seem to be a significant improvement and the F-Score is just a within-test percentage measure of how good the technique is at measuring sarcastic vs not sarcastic. Both of their unique features give improvements across all encodings, but they aren't additive and they differ depending on encoding. That may be statistically significant in some sense (regardless that the improvements are around 1%), but I don't see any statistical evaluation.

Table 2 apparently shows baselines for word2vec, but I expect the baselines are different and the improvements are relative to the appropriate baseline.

The model of how the features determine sarcasm is created using SVM ( https://en.wikipedia.org/wiki/Support_vector_machine ).

Can someone with experience in NLP comment on this paper?

P = precision R = recall F = F-score

F-score is just the harmonic mean of precision and recall.

https://en.wikipedia.org/wiki/Precision_and_recall

The paper isn't that extraordinary. Sarcasm detection is considered a hard problem. However, if you get a result that's "better" than some other published paper, you usually publish your work. You get (at most) a 5% boost in F-score using embedding features. Word embeddings are easy to work with, so it's not usually difficult to add them in with commonly used NLP features. You can give your model metrics an effortless bump.

Also, it looks like there could be a deep learning model that performs better already (https://techxplore.com/news/2016-08-deep-neural-network-appr...).

I've done two deep learning projects at work. I started learning about the techniques this past spring. They're ridiculously good, especially if you have a ton of data. Feature engineering (like in the paper) is usually a laborious process, and it often requires you to be knowledgeable in the problem domain. With neural nets if you have a reasonable architecture and choose sensible data representations (so your backpropagation converges), it can often "just work" without much tweaking.

“Vector Space Mathematics”? I think “linear algebra” is a more well-known term.

lol, yeah; ML seems to be an area where people like making new terms a lot.

It's been my experience that the most common cause of making up new terms is not knowing the existing ones.

Sometimes it is just a case of borrowing terms from one of several overlapping fields of knowledge as opposed to using a different term from one of the others. This is happening quite a lot in ML recently as ML borrows tools from classical AI, statistics (along with the arguments between Bayesians and frequentists), graph theory, information theory, game theory, neuroscience, software development, various branches of mathematics and geometry, and so on.

Borrowing the geometry-flavored term vector for the representation of a data point constituting a multidimensional magnitude+direction value[1] as opposed to the term explanatory variable from linear algebra/statistics is just happenstance, and is not an invention of a new term (or even a case of applying an existing term to something new).

Feature vectors and explanatory variables are, quite simply, two labels for the very same thing[2][3], and are often used interchangeably depending on context.

As for the familiarity argument, thinking of the data points as vectors is actually quite useful as a framing device for problem statements in terms of the tools and solutions it suggests[4].

[1] https://en.wikipedia.org/wiki/Feature_vector

[2] https://en.wikipedia.org/wiki/Vector_space

[3] https://en.wikipedia.org/wiki/Dependent_and_independent_vari...

[4] https://www.technologyreview.com/s/602025/how-vector-space-m...

The article's authors don't even seem to know what sarcasm is.

Their example “A woman needs a man like a fish needs a bicycle.” is not sarcastic so much as colorful analogy.

The researcher's word2vec approach is really a search for 'non-sequitur' -- any sentence that semantically goes of the rails at the end, it's probably better at detecting humor in general than 'sarcasm'

Mmm... Seems somewhat sarcastic to me in terms of subverting the simile-as-framing-device (in roughly the same way as "I need that like I need another hole in my head"). But in terms of which side of the fuzzy line between irony and sarcasm this lies, it may be closer to irony.

That's exactly what I thought when I saw the headline.

In this case, it is actually vector space mathematics (word2vec + the metric space approach they used) and not linear algebra.

The flaw in this approach is that many words do not have a single vector representation. Consider "Time flies like an arrow, fruit flies like a banana". The word "flies" has 2 distinct meanings; but the representation in word2vec will be a single, hybrid vector which is neither here nor there. And often sarcasm relies on this dual meaning to make a point.

space2vec can distinguish between multiple uses of the same word. I suspect that the current version would struggle with that sentence, but it's possible

[0] spacy.io

This is why I love it when I post a comment to HN and people can't tell if I'm being sarcastic or not. (https://news.ycombinator.com/item?id=12698590)

I'm thinking in the future, an adversarial neural network could be used for just this purpose. Take my sarcastic comment and alter it just enough so that it appears non-sarcastic or vis versa. It's kind of like using neural style to mess with the face recognition algorithms of social media profile pictures.

This seems like a good match of methodology to the problem.

Sarcasm seems rather adversarial by nature: the goal in delivery is often to signal, with intonation, only just enough to disambiguate intent. In highly competitive environments this results in deadpan delivery being common or even the default, and sarcasm having to be inferred entirely from content & context.

(consider the opposite: delivery with an exaggerated sarcastic tone: It's actually insulting and rude regardless of the content.)

People who haven't been exposed to such high sarcasm environments rely much more strongly on intonation, and correspondingly find it relatively difficult to identify sarcasm in text (again, this matches well with GAN failure modes).

OTOH, I have personally been inadvertently fooled the opposite way, listening to a speaker with growing joy at his elaborate windup of the surrounding audience in a social setting, only to realize "holy shit, this guy is actually seriously a neo-nazi advocating eugenics. Back away slowly."

Vector space is the place: https://www.google.com/?tbm=pts#tbm=pts&q=%22vector+space%22

I see vector space approaches as the tip of the spear when it comes to advancing NLP/AI.

are you kidding? what is the vector space being use in word2vec?